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---
comments: true
description: Discover YOLOv10, the latest in real-time object detection, eliminating NMS and boosting efficiency. Achieve top performance with a low computational cost.
keywords: YOLOv10, real-time object detection, NMS-free, deep learning, Tsinghua University, Ultralytics, machine learning, neural networks, performance optimization
---
# YOLOv10: Real-Time End-to-End [Object Detection](https://www.ultralytics.com/glossary/object-detection)
YOLOv10, built on the [Ultralytics](https://www.ultralytics.com/) [Python package](https://pypi.org/project/ultralytics/) by researchers at [Tsinghua University](https://www.tsinghua.edu.cn/en/), introduces a new approach to real-time object detection, addressing both the post-processing and model architecture deficiencies found in previous YOLO versions. By eliminating non-maximum suppression (NMS) and optimizing various model components, YOLOv10 achieves state-of-the-art performance with significantly reduced computational overhead. Extensive experiments demonstrate its superior accuracy-latency trade-offs across multiple model scales.
![YOLOv10 consistent dual assignment for NMS-free training](https://github.com/ultralytics/docs/releases/download/0/yolov10-consistent-dual-assignment.avif)
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/_gRqR-miFPE"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How to Train YOLOv10 on SKU-110k Dataset using Ultralytics | Retail Dataset
</p>
## Overview
Real-time object detection aims to accurately predict object categories and positions in images with low latency. The YOLO series has been at the forefront of this research due to its balance between performance and efficiency. However, reliance on NMS and architectural inefficiencies have hindered optimal performance. YOLOv10 addresses these issues by introducing consistent dual assignments for NMS-free training and a holistic efficiency-accuracy driven model design strategy.
### Architecture
The architecture of YOLOv10 builds upon the strengths of previous YOLO models while introducing several key innovations. The model architecture consists of the following components:
1. **Backbone**: Responsible for [feature extraction](https://www.ultralytics.com/glossary/feature-extraction), the backbone in YOLOv10 uses an enhanced version of CSPNet (Cross Stage Partial Network) to improve gradient flow and reduce computational redundancy.
2. **Neck**: The neck is designed to aggregate features from different scales and passes them to the head. It includes PAN (Path Aggregation Network) layers for effective multiscale feature fusion.
3. **One-to-Many Head**: Generates multiple predictions per object during training to provide rich supervisory signals and improve learning accuracy.
4. **One-to-One Head**: Generates a single best prediction per object during inference to eliminate the need for NMS, thereby reducing latency and improving efficiency.
## Key Features
1. **NMS-Free Training**: Utilizes consistent dual assignments to eliminate the need for NMS, reducing inference latency.
2. **Holistic Model Design**: Comprehensive optimization of various components from both efficiency and accuracy perspectives, including lightweight classification heads, spatial-channel decoupled down sampling, and rank-guided block design.
3. **Enhanced Model Capabilities**: Incorporates large-kernel convolutions and partial self-attention modules to improve performance without significant computational cost.
## Model Variants
YOLOv10 comes in various model scales to cater to different application needs:
- **YOLOv10-N**: Nano version for extremely resource-constrained environments.
- **YOLOv10-S**: Small version balancing speed and accuracy.
- **YOLOv10-M**: Medium version for general-purpose use.
- **YOLOv10-B**: Balanced version with increased width for higher accuracy.
- **YOLOv10-L**: Large version for higher accuracy at the cost of increased computational resources.
- **YOLOv10-X**: Extra-large version for maximum accuracy and performance.
## Performance
YOLOv10 outperforms previous YOLO versions and other state-of-the-art models in terms of accuracy and efficiency. For example, YOLOv10-S is 1.8x faster than RT-DETR-R18 with similar AP on the COCO dataset, and YOLOv10-B has 46% less latency and 25% fewer parameters than YOLOv9-C with the same performance.
| Model | Input Size | AP<sup>val</sup> | FLOPs (G) | Latency (ms) |
| -------------- | ---------- | ---------------- | --------- | ------------ |
| [YOLOv10-N][1] | 640 | 38.5 | **6.7** | **1.84** |
| [YOLOv10-S][2] | 640 | 46.3 | 21.6 | 2.49 |
| [YOLOv10-M][3] | 640 | 51.1 | 59.1 | 4.74 |
| [YOLOv10-B][4] | 640 | 52.5 | 92.0 | 5.74 |
| [YOLOv10-L][5] | 640 | 53.2 | 120.3 | 7.28 |
| [YOLOv10-X][6] | 640 | **54.4** | 160.4 | 10.70 |
Latency measured with TensorRT FP16 on T4 GPU.
## Methodology
### Consistent Dual Assignments for NMS-Free Training
YOLOv10 employs dual label assignments, combining one-to-many and one-to-one strategies during training to ensure rich supervision and efficient end-to-end deployment. The consistent matching metric aligns the supervision between both strategies, enhancing the quality of predictions during inference.
### Holistic Efficiency-[Accuracy](https://www.ultralytics.com/glossary/accuracy) Driven Model Design
#### Efficiency Enhancements
1. **Lightweight Classification Head**: Reduces the computational overhead of the classification head by using depth-wise separable convolutions.
2. **Spatial-Channel Decoupled Down sampling**: Decouples spatial reduction and channel modulation to minimize information loss and computational cost.
3. **Rank-Guided Block Design**: Adapts block design based on intrinsic stage redundancy, ensuring optimal parameter utilization.
#### Accuracy Enhancements
1. **Large-Kernel Convolution**: Enlarges the receptive field to enhance feature extraction capability.
2. **Partial Self-Attention (PSA)**: Incorporates self-attention modules to improve global representation learning with minimal overhead.
## Experiments and Results
YOLOv10 has been extensively tested on standard benchmarks like COCO, demonstrating superior performance and efficiency. The model achieves state-of-the-art results across different variants, showcasing significant improvements in latency and accuracy compared to previous versions and other contemporary detectors.
## Comparisons
![YOLOv10 comparison with SOTA object detectors](https://github.com/ultralytics/docs/releases/download/0/yolov10-comparison-sota-detectors.avif)
Compared to other state-of-the-art detectors:
- YOLOv10-S / X are 1.8× / 1.3× faster than RT-DETR-R18 / R101 with similar accuracy
- YOLOv10-B has 25% fewer parameters and 46% lower latency than YOLOv9-C at same accuracy
- YOLOv10-L / X outperform YOLOv8-L / X by 0.3 AP / 0.5 AP with 1.8× / 2.3× fewer parameters
Here is a detailed comparison of YOLOv10 variants with other state-of-the-art models:
| Model | Params<br><sup>(M) | FLOPs<br><sup>(G) | mAP<sup>val<br>50-95 | Latency<br><sup>(ms) | Latency-forward<br><sup>(ms) |
| ------------------ | ------------------ | ----------------- | -------------------- | -------------------- | ---------------------------- |
| YOLOv6-3.0-N | 4.7 | 11.4 | 37.0 | 2.69 | **1.76** |
| Gold-YOLO-N | 5.6 | 12.1 | **39.6** | 2.92 | 1.82 |
| YOLOv8-N | 3.2 | 8.7 | 37.3 | 6.16 | 1.77 |
| **[YOLOv10-N][1]** | **2.3** | **6.7** | 39.5 | **1.84** | 1.79 |
| | | | | | |
| YOLOv6-3.0-S | 18.5 | 45.3 | 44.3 | 3.42 | 2.35 |
| Gold-YOLO-S | 21.5 | 46.0 | 45.4 | 3.82 | 2.73 |
| YOLOv8-S | 11.2 | 28.6 | 44.9 | 7.07 | **2.33** |
| **[YOLOv10-S][2]** | **7.2** | **21.6** | **46.8** | **2.49** | 2.39 |
| | | | | | |
| RT-DETR-R18 | 20.0 | 60.0 | 46.5 | **4.58** | **4.49** |
| YOLOv6-3.0-M | 34.9 | 85.8 | 49.1 | 5.63 | 4.56 |
| Gold-YOLO-M | 41.3 | 87.5 | 49.8 | 6.38 | 5.45 |
| YOLOv8-M | 25.9 | 78.9 | 50.6 | 9.50 | 5.09 |
| **[YOLOv10-M][3]** | **15.4** | **59.1** | **51.3** | 4.74 | 4.63 |
| | | | | | |
| YOLOv6-3.0-L | 59.6 | 150.7 | 51.8 | 9.02 | 7.90 |
| Gold-YOLO-L | 75.1 | 151.7 | 51.8 | 10.65 | 9.78 |
| YOLOv8-L | 43.7 | 165.2 | 52.9 | 12.39 | 8.06 |
| RT-DETR-R50 | 42.0 | 136.0 | 53.1 | 9.20 | 9.07 |
| **[YOLOv10-L][5]** | **24.4** | **120.3** | **53.4** | **7.28** | **7.21** |
| | | | | | |
| YOLOv8-X | 68.2 | 257.8 | 53.9 | 16.86 | 12.83 |
| RT-DETR-R101 | 76.0 | 259.0 | 54.3 | 13.71 | 13.58 |
| **[YOLOv10-X][6]** | **29.5** | **160.4** | **54.4** | **10.70** | **10.60** |
[1]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10n.pt
[2]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10s.pt
[3]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10m.pt
[4]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10b.pt
[5]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10l.pt
[6]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10x.pt
## Usage Examples
For predicting new images with YOLOv10:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a pre-trained YOLOv10n model
model = YOLO("yolov10n.pt")
# Perform object detection on an image
results = model("image.jpg")
# Display the results
results[0].show()
```
=== "CLI"
```bash
# Load a COCO-pretrained YOLOv10n model and run inference on the 'bus.jpg' image
yolo detect predict model=yolov10n.pt source=path/to/bus.jpg
```
For training YOLOv10 on a custom dataset:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load YOLOv10n model from scratch
model = YOLO("yolov10n.yaml")
# Train the model
model.train(data="coco8.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Build a YOLOv10n model from scratch and train it on the COCO8 example dataset for 100 epochs
yolo train model=yolov10n.yaml data=coco8.yaml epochs=100 imgsz=640
# Build a YOLOv10n model from scratch and run inference on the 'bus.jpg' image
yolo predict model=yolov10n.yaml source=path/to/bus.jpg
```
## Supported Tasks and Modes
The YOLOv10 models series offers a range of models, each optimized for high-performance [Object Detection](../tasks/detect.md). These models cater to varying computational needs and accuracy requirements, making them versatile for a wide array of applications.
| Model | Filenames | Tasks | Inference | Validation | Training | Export |
| ------- | --------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv10 | `yolov10n.pt` `yolov10s.pt` `yolov10m.pt` `yolov10l.pt` `yolov10x.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
## Exporting YOLOv10
Due to the new operations introduced with YOLOv10, not all export formats provided by Ultralytics are currently supported. The following table outlines which formats have been successfully converted using Ultralytics for YOLOv10. Feel free to open a pull request if you're able to [provide a contribution change](../help/contributing.md) for adding export support of additional formats for YOLOv10.
| Export Format | Export Support | Exported Model Inference | Notes |
| ------------------------------------------------- | -------------- | ------------------------ | -------------------------------------------------------------------------------------- |
| [TorchScript](../integrations/torchscript.md) | ✅ | ✅ | Standard [PyTorch](https://www.ultralytics.com/glossary/pytorch) model format. |
| [ONNX](../integrations/onnx.md) | ✅ | ✅ | Widely supported for deployment. |
| [OpenVINO](../integrations/openvino.md) | ✅ | ✅ | Optimized for Intel hardware. |
| [TensorRT](../integrations/tensorrt.md) | ✅ | ✅ | Optimized for NVIDIA GPUs. |
| [CoreML](../integrations/coreml.md) | ✅ | ✅ | Limited to Apple devices. |
| [TF SavedModel](../integrations/tf-savedmodel.md) | ✅ | ✅ | [TensorFlow](https://www.ultralytics.com/glossary/tensorflow)'s standard model format. |
| [TF GraphDef](../integrations/tf-graphdef.md) | ✅ | ✅ | Legacy TensorFlow format. |
| [TF Lite](../integrations/tflite.md) | ✅ | ✅ | Optimized for mobile and embedded. |
| [TF Edge TPU](../integrations/edge-tpu.md) | ✅ | ✅ | Specific to Google's Edge TPU devices. |
| [TF.js](../integrations/tfjs.md) | ✅ | ✅ | JavaScript environment for browser use. |
| [PaddlePaddle](../integrations/paddlepaddle.md) | ❌ | ❌ | Popular in China; less global support. |
| [NCNN](../integrations/ncnn.md) | ✅ | ❌ | Layer `torch.topk` not exists or registered |
## Conclusion
YOLOv10 sets a new standard in real-time object detection by addressing the shortcomings of previous YOLO versions and incorporating innovative design strategies. Its ability to deliver high accuracy with low computational cost makes it an ideal choice for a wide range of real-world applications.
## Citations and Acknowledgements
We would like to acknowledge the YOLOv10 authors from [Tsinghua University](https://www.tsinghua.edu.cn/en/) for their extensive research and significant contributions to the [Ultralytics](https://www.ultralytics.com/) framework:
!!! quote ""
=== "BibTeX"
```bibtex
@article{THU-MIGyolov10,
title={YOLOv10: Real-Time End-to-End Object Detection},
author={Ao Wang, Hui Chen, Lihao Liu, et al.},
journal={arXiv preprint arXiv:2405.14458},
year={2024},
institution={Tsinghua University},
license = {AGPL-3.0}
}
```
For detailed implementation, architectural innovations, and experimental results, please refer to the YOLOv10 [research paper](https://arxiv.org/pdf/2405.14458) and [GitHub repository](https://github.com/THU-MIG/yolov10) by the Tsinghua University team.
## FAQ
### What is YOLOv10 and how does it differ from previous YOLO versions?
YOLOv10, developed by researchers at [Tsinghua University](https://www.tsinghua.edu.cn/en/), introduces several key innovations to real-time object detection. It eliminates the need for non-maximum suppression (NMS) by employing consistent dual assignments during training and optimized model components for superior performance with reduced computational overhead. For more details on its architecture and key features, check out the [YOLOv10 overview](#overview) section.
### How can I get started with running inference using YOLOv10?
For easy inference, you can use the Ultralytics YOLO Python library or the command line interface (CLI). Below are examples of predicting new images using YOLOv10:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load the pre-trained YOLOv10-N model
model = YOLO("yolov10n.pt")
results = model("image.jpg")
results[0].show()
```
=== "CLI"
```bash
yolo detect predict model=yolov10n.pt source=path/to/image.jpg
```
For more usage examples, visit our [Usage Examples](#usage-examples) section.
### Which model variants does YOLOv10 offer and what are their use cases?
YOLOv10 offers several model variants to cater to different use cases:
- **YOLOv10-N**: Suitable for extremely resource-constrained environments
- **YOLOv10-S**: Balances speed and accuracy
- **YOLOv10-M**: General-purpose use
- **YOLOv10-B**: Higher accuracy with increased width
- **YOLOv10-L**: High accuracy at the cost of computational resources
- **YOLOv10-X**: Maximum accuracy and performance
Each variant is designed for different computational needs and accuracy requirements, making them versatile for a variety of applications. Explore the [Model Variants](#model-variants) section for more information.
### How does the NMS-free approach in YOLOv10 improve performance?
YOLOv10 eliminates the need for non-maximum suppression (NMS) during inference by employing consistent dual assignments for training. This approach reduces inference latency and enhances prediction efficiency. The architecture also includes a one-to-one head for inference, ensuring that each object gets a single best prediction. For a detailed explanation, see the [Consistent Dual Assignments for NMS-Free Training](#consistent-dual-assignments-for-nms-free-training) section.
### Where can I find the export options for YOLOv10 models?
YOLOv10 supports several export formats, including TorchScript, ONNX, OpenVINO, and TensorRT. However, not all export formats provided by Ultralytics are currently supported for YOLOv10 due to its new operations. For details on the supported formats and instructions on exporting, visit the [Exporting YOLOv10](#exporting-yolov10) section.
### What are the performance benchmarks for YOLOv10 models?
YOLOv10 outperforms previous YOLO versions and other state-of-the-art models in both accuracy and efficiency. For example, YOLOv10-S is 1.8x faster than RT-DETR-R18 with a similar AP on the COCO dataset. YOLOv10-B shows 46% less latency and 25% fewer parameters than YOLOv9-C with the same performance. Detailed benchmarks can be found in the [Comparisons](#comparisons) section.
docs/en/models/yolov3.md 0 → 100644
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---
comments: true
description: Discover YOLOv3 and its variants YOLOv3-Ultralytics and YOLOv3u. Learn about their features, implementations, and support for object detection tasks.
keywords: YOLOv3, YOLOv3-Ultralytics, YOLOv3u, object detection, Ultralytics, computer vision, AI models, deep learning
---
# YOLOv3, YOLOv3-Ultralytics, and YOLOv3u
## Overview
This document presents an overview of three closely related object detection models, namely [YOLOv3](https://pjreddie.com/darknet/yolo/), [YOLOv3-Ultralytics](https://github.com/ultralytics/yolov3), and [YOLOv3u](https://github.com/ultralytics/ultralytics).
1. **YOLOv3:** This is the third version of the You Only Look Once (YOLO) object detection algorithm. Originally developed by Joseph Redmon, YOLOv3 improved on its predecessors by introducing features such as multiscale predictions and three different sizes of detection kernels.
2. **YOLOv3-Ultralytics:** This is Ultralytics' implementation of the YOLOv3 model. It reproduces the original YOLOv3 architecture and offers additional functionalities, such as support for more pre-trained models and easier customization options.
3. **YOLOv3u:** This is an updated version of YOLOv3-Ultralytics that incorporates the anchor-free, objectness-free split head used in YOLOv8 models. YOLOv3u maintains the same backbone and neck architecture as YOLOv3 but with the updated detection head from YOLOv8.
![Ultralytics YOLOv3](https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov3-banner.avif)
## Key Features
- **YOLOv3:** Introduced the use of three different scales for detection, leveraging three different sizes of detection kernels: 13x13, 26x26, and 52x52. This significantly improved detection accuracy for objects of different sizes. Additionally, YOLOv3 added features such as multi-label predictions for each [bounding box](https://www.ultralytics.com/glossary/bounding-box) and a better feature extractor network.
- **YOLOv3-Ultralytics:** Ultralytics' implementation of YOLOv3 provides the same performance as the original model but comes with added support for more pre-trained models, additional training methods, and easier customization options. This makes it more versatile and user-friendly for practical applications.
- **YOLOv3u:** This updated model incorporates the anchor-free, objectness-free split head from YOLOv8. By eliminating the need for pre-defined anchor boxes and objectness scores, this detection head design can improve the model's ability to detect objects of varying sizes and shapes. This makes YOLOv3u more robust and accurate for object detection tasks.
## Supported Tasks and Modes
The YOLOv3 series, including YOLOv3, YOLOv3-Ultralytics, and YOLOv3u, are designed specifically for object detection tasks. These models are renowned for their effectiveness in various real-world scenarios, balancing accuracy and speed. Each variant offers unique features and optimizations, making them suitable for a range of applications.
All three models support a comprehensive set of modes, ensuring versatility in various stages of [model deployment](https://www.ultralytics.com/glossary/model-deployment) and development. These modes include [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), providing users with a complete toolkit for effective object detection.
| Model Type | Tasks Supported | Inference | Validation | Training | Export |
| ------------------ | -------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv3 | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv3-Ultralytics | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv3u | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
This table provides an at-a-glance view of the capabilities of each YOLOv3 variant, highlighting their versatility and suitability for various tasks and operational modes in object detection workflows.
## Usage Examples
This example provides simple YOLOv3 training and inference examples. For full documentation on these and other [modes](../modes/index.md) see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md) docs pages.
!!! example
=== "Python"
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
```python
from ultralytics import YOLO
# Load a COCO-pretrained YOLOv3n model
model = YOLO("yolov3n.pt")
# Display model information (optional)
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
# Run inference with the YOLOv3n model on the 'bus.jpg' image
results = model("path/to/bus.jpg")
```
=== "CLI"
CLI commands are available to directly run the models:
```bash
# Load a COCO-pretrained YOLOv3n model and train it on the COCO8 example dataset for 100 epochs
yolo train model=yolov3n.pt data=coco8.yaml epochs=100 imgsz=640
# Load a COCO-pretrained YOLOv3n model and run inference on the 'bus.jpg' image
yolo predict model=yolov3n.pt source=path/to/bus.jpg
```
## Citations and Acknowledgements
If you use YOLOv3 in your research, please cite the original YOLO papers and the Ultralytics YOLOv3 repository:
!!! quote ""
=== "BibTeX"
```bibtex
@article{redmon2018yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal={arXiv preprint arXiv:1804.02767},
year={2018}
}
```
Thank you to Joseph Redmon and Ali Farhadi for developing the original YOLOv3.
## FAQ
### What are the differences between YOLOv3, YOLOv3-Ultralytics, and YOLOv3u?
YOLOv3 is the third iteration of the YOLO (You Only Look Once) [object detection](https://www.ultralytics.com/glossary/object-detection) algorithm developed by Joseph Redmon, known for its balance of [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed, utilizing three different scales (13x13, 26x26, and 52x52) for detections. YOLOv3-Ultralytics is Ultralytics' adaptation of YOLOv3 that adds support for more pre-trained models and facilitates easier model customization. YOLOv3u is an upgraded variant of YOLOv3-Ultralytics, integrating the anchor-free, objectness-free split head from YOLOv8, improving detection robustness and accuracy for various object sizes. For more details on the variants, refer to the [YOLOv3 series](https://github.com/ultralytics/yolov3).
### How can I train a YOLOv3 model using Ultralytics?
Training a YOLOv3 model with Ultralytics is straightforward. You can train the model using either Python or CLI:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a COCO-pretrained YOLOv3n model
model = YOLO("yolov3n.pt")
# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Load a COCO-pretrained YOLOv3n model and train it on the COCO8 example dataset for 100 epochs
yolo train model=yolov3n.pt data=coco8.yaml epochs=100 imgsz=640
```
For more comprehensive training options and guidelines, visit our [Train mode documentation](../modes/train.md).
### What makes YOLOv3u more accurate for object detection tasks?
YOLOv3u improves upon YOLOv3 and YOLOv3-Ultralytics by incorporating the anchor-free, objectness-free split head used in YOLOv8 models. This upgrade eliminates the need for pre-defined anchor boxes and objectness scores, enhancing its capability to detect objects of varying sizes and shapes more precisely. This makes YOLOv3u a better choice for complex and diverse object detection tasks. For more information, refer to the [Why YOLOv3u](#overview) section.
### How can I use YOLOv3 models for inference?
You can perform inference using YOLOv3 models by either Python scripts or CLI commands:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a COCO-pretrained YOLOv3n model
model = YOLO("yolov3n.pt")
# Run inference with the YOLOv3n model on the 'bus.jpg' image
results = model("path/to/bus.jpg")
```
=== "CLI"
```bash
# Load a COCO-pretrained YOLOv3n model and run inference on the 'bus.jpg' image
yolo predict model=yolov3n.pt source=path/to/bus.jpg
```
Refer to the [Inference mode documentation](../modes/predict.md) for more details on running YOLO models.
### What tasks are supported by YOLOv3 and its variants?
YOLOv3, YOLOv3-Ultralytics, and YOLOv3u primarily support object detection tasks. These models can be used for various stages of model deployment and development, such as Inference, Validation, Training, and Export. For a comprehensive set of tasks supported and more in-depth details, visit our [Object Detection tasks documentation](../tasks/detect.md).
### Where can I find resources to cite YOLOv3 in my research?
If you use YOLOv3 in your research, please cite the original YOLO papers and the Ultralytics YOLOv3 repository. Example BibTeX citation:
!!! quote ""
=== "BibTeX"
```bibtex
@article{redmon2018yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal={arXiv preprint arXiv:1804.02767},
year={2018}
}
```
For more citation details, refer to the [Citations and Acknowledgements](#citations-and-acknowledgements) section.
docs/en/models/yolov4.md 0 → 100644
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---
comments: true
description: Explore YOLOv4, a state-of-the-art real-time object detection model by Alexey Bochkovskiy. Discover its architecture, features, and performance.
keywords: YOLOv4, object detection, real-time detection, Alexey Bochkovskiy, neural networks, machine learning, computer vision
---
# YOLOv4: High-Speed and Precise Object Detection
Welcome to the Ultralytics documentation page for YOLOv4, a state-of-the-art, real-time object detector launched in 2020 by Alexey Bochkovskiy at [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet). YOLOv4 is designed to provide the optimal balance between speed and accuracy, making it an excellent choice for many applications.
![YOLOv4 architecture diagram](https://github.com/ultralytics/docs/releases/download/0/yolov4-architecture-diagram.avif) **YOLOv4 architecture diagram**. Showcasing the intricate network design of YOLOv4, including the backbone, neck, and head components, and their interconnected layers for optimal real-time object detection.
## Introduction
YOLOv4 stands for You Only Look Once version 4. It is a real-time object detection model developed to address the limitations of previous YOLO versions like [YOLOv3](yolov3.md) and other object detection models. Unlike other [convolutional neural network](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNN) based object detectors, YOLOv4 is not only applicable for recommendation systems but also for standalone process management and human input reduction. Its operation on conventional graphics processing units (GPUs) allows for mass usage at an affordable price, and it is designed to work in real-time on a conventional GPU while requiring only one such GPU for training.
## Architecture
YOLOv4 makes use of several innovative features that work together to optimize its performance. These include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT), Mish-activation, Mosaic [data augmentation](https://www.ultralytics.com/glossary/data-augmentation), DropBlock [regularization](https://www.ultralytics.com/glossary/regularization), and CIoU loss. These features are combined to achieve state-of-the-art results.
A typical object detector is composed of several parts including the input, the backbone, the neck, and the head. The backbone of YOLOv4 is pre-trained on ImageNet and is used to predict classes and bounding boxes of objects. The backbone could be from several models including VGG, ResNet, ResNeXt, or DenseNet. The neck part of the detector is used to collect feature maps from different stages and usually includes several bottom-up paths and several top-down paths. The head part is what is used to make the final object detections and classifications.
## Bag of Freebies
YOLOv4 also makes use of methods known as "bag of freebies," which are techniques that improve the accuracy of the model during training without increasing the cost of inference. Data augmentation is a common bag of freebies technique used in object detection, which increases the variability of the input images to improve the robustness of the model. Some examples of data augmentation include photometric distortions (adjusting the brightness, contrast, hue, saturation, and noise of an image) and geometric distortions (adding random scaling, cropping, flipping, and rotating). These techniques help the model to generalize better to different types of images.
## Features and Performance
YOLOv4 is designed for optimal speed and accuracy in object detection. The architecture of YOLOv4 includes CSPDarknet53 as the backbone, PANet as the neck, and YOLOv3 as the detection head. This design allows YOLOv4 to perform object detection at an impressive speed, making it suitable for real-time applications. YOLOv4 also excels in accuracy, achieving state-of-the-art results in object detection benchmarks.
## Usage Examples
As of the time of writing, Ultralytics does not currently support YOLOv4 models. Therefore, any users interested in using YOLOv4 will need to refer directly to the YOLOv4 GitHub repository for installation and usage instructions.
Here is a brief overview of the typical steps you might take to use YOLOv4:
1. Visit the YOLOv4 GitHub repository: [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet).
2. Follow the instructions provided in the README file for installation. This typically involves cloning the repository, installing necessary dependencies, and setting up any necessary environment variables.
3. Once installation is complete, you can train and use the model as per the usage instructions provided in the repository. This usually involves preparing your dataset, configuring the model parameters, training the model, and then using the trained model to perform object detection.
Please note that the specific steps may vary depending on your specific use case and the current state of the YOLOv4 repository. Therefore, it is strongly recommended to refer directly to the instructions provided in the YOLOv4 GitHub repository.
We regret any inconvenience this may cause and will strive to update this document with usage examples for Ultralytics once support for YOLOv4 is implemented.
## Conclusion
YOLOv4 is a powerful and efficient object detection model that strikes a balance between speed and accuracy. Its use of unique features and bag of freebies techniques during training allows it to perform excellently in real-time object detection tasks. YOLOv4 can be trained and used by anyone with a conventional GPU, making it accessible and practical for a wide range of applications.
## Citations and Acknowledgements
We would like to acknowledge the YOLOv4 authors for their significant contributions in the field of real-time object detection:
!!! quote ""
=== "BibTeX"
```bibtex
@misc{bochkovskiy2020yolov4,
title={YOLOv4: Optimal Speed and Accuracy of Object Detection},
author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
year={2020},
eprint={2004.10934},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
The original YOLOv4 paper can be found on [arXiv](https://arxiv.org/abs/2004.10934). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/AlexeyAB/darknet). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
## FAQ
### What is YOLOv4 and why should I use it for [object detection](https://www.ultralytics.com/glossary/object-detection)?
YOLOv4, which stands for "You Only Look Once version 4," is a state-of-the-art real-time object detection model developed by Alexey Bochkovskiy in 2020. It achieves an optimal balance between speed and [accuracy](https://www.ultralytics.com/glossary/accuracy), making it highly suitable for real-time applications. YOLOv4's architecture incorporates several innovative features like Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), and Self-adversarial-training (SAT), among others, to achieve state-of-the-art results. If you're looking for a high-performance model that operates efficiently on conventional GPUs, YOLOv4 is an excellent choice.
### How does the architecture of YOLOv4 enhance its performance?
The architecture of YOLOv4 includes several key components: the backbone, the neck, and the head. The backbone, which can be models like VGG, ResNet, or CSPDarknet53, is pre-trained to predict classes and bounding boxes. The neck, utilizing PANet, connects feature maps from different stages for comprehensive data extraction. Finally, the head, which uses configurations from YOLOv3, makes the final object detections. YOLOv4 also employs "bag of freebies" techniques like mosaic data augmentation and DropBlock regularization, further optimizing its speed and accuracy.
### What are "bag of freebies" in the context of YOLOv4?
"Bag of freebies" refers to methods that improve the training accuracy of YOLOv4 without increasing the cost of inference. These techniques include various forms of data augmentation like photometric distortions (adjusting brightness, contrast, etc.) and geometric distortions (scaling, cropping, flipping, rotating). By increasing the variability of the input images, these augmentations help YOLOv4 generalize better to different types of images, thereby improving its robustness and accuracy without compromising its real-time performance.
### Why is YOLOv4 considered suitable for real-time object detection on conventional GPUs?
YOLOv4 is designed to optimize both speed and accuracy, making it ideal for real-time object detection tasks that require quick and reliable performance. It operates efficiently on conventional GPUs, needing only one for both training and inference. This makes it accessible and practical for various applications ranging from [recommendation systems](https://www.ultralytics.com/glossary/recommendation-system) to standalone process management, thereby reducing the need for extensive hardware setups and making it a cost-effective solution for real-time object detection.
### How can I get started with YOLOv4 if Ultralytics does not currently support it?
To get started with YOLOv4, you should visit the official [YOLOv4 GitHub repository](https://github.com/AlexeyAB/darknet). Follow the installation instructions provided in the README file, which typically include cloning the repository, installing dependencies, and setting up environment variables. Once installed, you can train the model by preparing your dataset, configuring the model parameters, and following the usage instructions provided. Since Ultralytics does not currently support YOLOv4, it is recommended to refer directly to the YOLOv4 GitHub for the most up-to-date and detailed guidance.
docs/en/models/yolov5.md 0 → 100644
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---
comments: true
description: Explore YOLOv5u, an advanced object detection model with optimized accuracy-speed tradeoff, featuring anchor-free Ultralytics head and various pre-trained models.
keywords: YOLOv5, YOLOv5u, object detection, Ultralytics, anchor-free, pre-trained models, accuracy, speed, real-time detection
---
# Ultralytics YOLOv5
## Overview
YOLOv5u represents an advancement in [object detection](https://www.ultralytics.com/glossary/object-detection) methodologies. Originating from the foundational architecture of the [YOLOv5](https://github.com/ultralytics/yolov5) model developed by Ultralytics, YOLOv5u integrates the anchor-free, objectness-free split head, a feature previously introduced in the [YOLOv8](yolov8.md) models. This adaptation refines the model's architecture, leading to an improved accuracy-speed tradeoff in object detection tasks. Given the empirical results and its derived features, YOLOv5u provides an efficient alternative for those seeking robust solutions in both research and practical applications.
![Ultralytics YOLOv5](https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov5-splash.avif)
## Key Features
- **Anchor-free Split Ultralytics Head:** Traditional object detection models rely on predefined anchor boxes to predict object locations. However, YOLOv5u modernizes this approach. By adopting an anchor-free split Ultralytics head, it ensures a more flexible and adaptive detection mechanism, consequently enhancing the performance in diverse scenarios.
- **Optimized Accuracy-Speed Tradeoff:** Speed and accuracy often pull in opposite directions. But YOLOv5u challenges this tradeoff. It offers a calibrated balance, ensuring real-time detections without compromising on accuracy. This feature is particularly invaluable for applications that demand swift responses, such as autonomous vehicles, robotics, and real-time video analytics.
- **Variety of Pre-trained Models:** Understanding that different tasks require different toolsets, YOLOv5u provides a plethora of pre-trained models. Whether you're focusing on Inference, Validation, or Training, there's a tailor-made model awaiting you. This variety ensures you're not just using a one-size-fits-all solution, but a model specifically fine-tuned for your unique challenge.
## Supported Tasks and Modes
The YOLOv5u models, with various pre-trained weights, excel in [Object Detection](../tasks/detect.md) tasks. They support a comprehensive range of modes, making them suitable for diverse applications, from development to deployment.
| Model Type | Pre-trained Weights | Task | Inference | Validation | Training | Export |
| ---------- | --------------------------------------------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv5u | `yolov5nu`, `yolov5su`, `yolov5mu`, `yolov5lu`, `yolov5xu`, `yolov5n6u`, `yolov5s6u`, `yolov5m6u`, `yolov5l6u`, `yolov5x6u` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
This table provides a detailed overview of the YOLOv5u model variants, highlighting their applicability in object detection tasks and support for various operational modes such as [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This comprehensive support ensures that users can fully leverage the capabilities of YOLOv5u models in a wide range of object detection scenarios.
## Performance Metrics
!!! performance
=== "Detection"
See [Detection Docs](../tasks/detect.md) for usage examples with these models trained on [COCO](../datasets/detect/coco.md), which include 80 pre-trained classes.
| Model | YAML | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|---------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|-----------------------|----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
| [yolov5nu.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5nu.pt) | [yolov5n.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 34.3 | 73.6 | 1.06 | 2.6 | 7.7 |
| [yolov5su.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5su.pt) | [yolov5s.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 43.0 | 120.7 | 1.27 | 9.1 | 24.0 |
| [yolov5mu.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5mu.pt) | [yolov5m.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 49.0 | 233.9 | 1.86 | 25.1 | 64.2 |
| [yolov5lu.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5lu.pt) | [yolov5l.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 52.2 | 408.4 | 2.50 | 53.2 | 135.0 |
| [yolov5xu.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5xu.pt) | [yolov5x.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 53.2 | 763.2 | 3.81 | 97.2 | 246.4 |
| | | | | | | | |
| [yolov5n6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5n6u.pt) | [yolov5n6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 42.1 | 211.0 | 1.83 | 4.3 | 7.8 |
| [yolov5s6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5s6u.pt) | [yolov5s6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 48.6 | 422.6 | 2.34 | 15.3 | 24.6 |
| [yolov5m6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5m6u.pt) | [yolov5m6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 53.6 | 810.9 | 4.36 | 41.2 | 65.7 |
| [yolov5l6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5l6u.pt) | [yolov5l6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 55.7 | 1470.9 | 5.47 | 86.1 | 137.4 |
| [yolov5x6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5x6u.pt) | [yolov5x6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 56.8 | 2436.5 | 8.98 | 155.4 | 250.7 |
## Usage Examples
This example provides simple YOLOv5 training and inference examples. For full documentation on these and other [modes](../modes/index.md) see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md) docs pages.
!!! example
=== "Python"
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
```python
from ultralytics import YOLO
# Load a COCO-pretrained YOLOv5n model
model = YOLO("yolov5n.pt")
# Display model information (optional)
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
# Run inference with the YOLOv5n model on the 'bus.jpg' image
results = model("path/to/bus.jpg")
```
=== "CLI"
CLI commands are available to directly run the models:
```bash
# Load a COCO-pretrained YOLOv5n model and train it on the COCO8 example dataset for 100 epochs
yolo train model=yolov5n.pt data=coco8.yaml epochs=100 imgsz=640
# Load a COCO-pretrained YOLOv5n model and run inference on the 'bus.jpg' image
yolo predict model=yolov5n.pt source=path/to/bus.jpg
```
## Citations and Acknowledgements
!!! tip "Ultralytics YOLOv5 Publication"
Ultralytics has not published a formal research paper for YOLOv5 due to the rapidly evolving nature of the models. We focus on advancing the technology and making it easier to use, rather than producing static documentation. For the most up-to-date information on YOLO architecture, features, and usage, please refer to our [GitHub repository](https://github.com/ultralytics/ultralytics) and [documentation](https://docs.ultralytics.com/).
If you use YOLOv5 or YOLOv5u in your research, please cite the Ultralytics YOLOv5 repository as follows:
!!! quote ""
=== "BibTeX"
```bibtex
@software{yolov5,
title = {Ultralytics YOLOv5},
author = {Glenn Jocher},
year = {2020},
version = {7.0},
license = {AGPL-3.0},
url = {https://github.com/ultralytics/yolov5},
doi = {10.5281/zenodo.3908559},
orcid = {0000-0001-5950-6979}
}
```
Please note that YOLOv5 models are provided under [AGPL-3.0](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) and [Enterprise](https://www.ultralytics.com/license) licenses.
## FAQ
### What is Ultralytics YOLOv5u and how does it differ from YOLOv5?
Ultralytics YOLOv5u is an advanced version of YOLOv5, integrating the anchor-free, objectness-free split head that enhances the [accuracy](https://www.ultralytics.com/glossary/accuracy)-speed tradeoff for real-time object detection tasks. Unlike the traditional YOLOv5, YOLOv5u adopts an anchor-free detection mechanism, making it more flexible and adaptive in diverse scenarios. For more detailed information on its features, you can refer to the [YOLOv5 Overview](#overview).
### How does the anchor-free Ultralytics head improve object detection performance in YOLOv5u?
The anchor-free Ultralytics head in YOLOv5u improves object detection performance by eliminating the dependency on predefined anchor boxes. This results in a more flexible and adaptive detection mechanism that can handle various object sizes and shapes with greater efficiency. This enhancement directly contributes to a balanced tradeoff between accuracy and speed, making YOLOv5u suitable for real-time applications. Learn more about its architecture in the [Key Features](#key-features) section.
### Can I use pre-trained YOLOv5u models for different tasks and modes?
Yes, you can use pre-trained YOLOv5u models for various tasks such as [Object Detection](../tasks/detect.md). These models support multiple modes, including [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This flexibility allows users to leverage the capabilities of YOLOv5u models across different operational requirements. For a detailed overview, check the [Supported Tasks and Modes](#supported-tasks-and-modes) section.
### How do the performance metrics of YOLOv5u models compare on different platforms?
The performance metrics of YOLOv5u models vary depending on the platform and hardware used. For example, the YOLOv5nu model achieves a 34.3 mAP on COCO dataset with a speed of 73.6 ms on CPU (ONNX) and 1.06 ms on A100 TensorRT. Detailed performance metrics for different YOLOv5u models can be found in the [Performance Metrics](#performance-metrics) section, which provides a comprehensive comparison across various devices.
### How can I train a YOLOv5u model using the Ultralytics Python API?
You can train a YOLOv5u model by loading a pre-trained model and running the training command with your dataset. Here's a quick example:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a COCO-pretrained YOLOv5n model
model = YOLO("yolov5n.pt")
# Display model information (optional)
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Load a COCO-pretrained YOLOv5n model and train it on the COCO8 example dataset for 100 epochs
yolo train model=yolov5n.pt data=coco8.yaml epochs=100 imgsz=640
```
For more detailed instructions, visit the [Usage Examples](#usage-examples) section.
docs/en/models/yolov6.md 0 → 100644
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---
comments: true
description: Explore Meituan YOLOv6, a top-tier object detector balancing speed and accuracy. Learn about its unique features and performance metrics on Ultralytics Docs.
keywords: Meituan YOLOv6, object detection, real-time applications, BiC module, Anchor-Aided Training, COCO dataset, high-performance models, Ultralytics Docs
---
# Meituan YOLOv6
## Overview
[Meituan](https://about.meituan.com/) YOLOv6 is a cutting-edge object detector that offers remarkable balance between speed and accuracy, making it a popular choice for real-time applications. This model introduces several notable enhancements on its architecture and training scheme, including the implementation of a Bi-directional Concatenation (BiC) module, an anchor-aided training (AAT) strategy, and an improved backbone and neck design for state-of-the-art accuracy on the COCO dataset.
![Meituan YOLOv6](https://github.com/ultralytics/docs/releases/download/0/meituan-yolov6.avif)
![Model example image](https://github.com/ultralytics/docs/releases/download/0/yolov6-architecture-diagram.avif) **Overview of YOLOv6.** Model architecture diagram showing the redesigned network components and training strategies that have led to significant performance improvements. (a) The neck of YOLOv6 (N and S are shown). Note for M/L, RepBlocks is replaced with CSPStackRep. (b) The structure of a BiC module. (c) A SimCSPSPPF block. ([source](https://arxiv.org/pdf/2301.05586.pdf)).
### Key Features
- **Bidirectional Concatenation (BiC) Module:** YOLOv6 introduces a BiC module in the neck of the detector, enhancing localization signals and delivering performance gains with negligible speed degradation.
- **Anchor-Aided Training (AAT) Strategy:** This model proposes AAT to enjoy the benefits of both anchor-based and anchor-free paradigms without compromising inference efficiency.
- **Enhanced Backbone and Neck Design:** By deepening YOLOv6 to include another stage in the backbone and neck, this model achieves state-of-the-art performance on the COCO dataset at high-resolution input.
- **Self-Distillation Strategy:** A new self-distillation strategy is implemented to boost the performance of smaller models of YOLOv6, enhancing the auxiliary regression branch during training and removing it at inference to avoid a marked speed decline.
## Performance Metrics
YOLOv6 provides various pre-trained models with different scales:
- YOLOv6-N: 37.5% AP on COCO val2017 at 1187 FPS with NVIDIA T4 GPU.
- YOLOv6-S: 45.0% AP at 484 FPS.
- YOLOv6-M: 50.0% AP at 226 FPS.
- YOLOv6-L: 52.8% AP at 116 FPS.
- YOLOv6-L6: State-of-the-art accuracy in real-time.
YOLOv6 also provides quantized models for different [precisions](https://www.ultralytics.com/glossary/precision) and models optimized for mobile platforms.
## Usage Examples
This example provides simple YOLOv6 training and inference examples. For full documentation on these and other [modes](../modes/index.md) see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md) docs pages.
!!! example
=== "Python"
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
```python
from ultralytics import YOLO
# Build a YOLOv6n model from scratch
model = YOLO("yolov6n.yaml")
# Display model information (optional)
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
# Run inference with the YOLOv6n model on the 'bus.jpg' image
results = model("path/to/bus.jpg")
```
=== "CLI"
CLI commands are available to directly run the models:
```bash
# Build a YOLOv6n model from scratch and train it on the COCO8 example dataset for 100 epochs
yolo train model=yolov6n.yaml data=coco8.yaml epochs=100 imgsz=640
# Build a YOLOv6n model from scratch and run inference on the 'bus.jpg' image
yolo predict model=yolov6n.yaml source=path/to/bus.jpg
```
## Supported Tasks and Modes
The YOLOv6 series offers a range of models, each optimized for high-performance [Object Detection](../tasks/detect.md). These models cater to varying computational needs and [accuracy](https://www.ultralytics.com/glossary/accuracy) requirements, making them versatile for a wide array of applications.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
| ---------- | ------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv6-N | `yolov6-n.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-S | `yolov6-s.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-M | `yolov6-m.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-L | `yolov6-l.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-L6 | `yolov6-l6.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
This table provides a detailed overview of the YOLOv6 model variants, highlighting their capabilities in [object detection](https://www.ultralytics.com/glossary/object-detection) tasks and their compatibility with various operational modes such as [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This comprehensive support ensures that users can fully leverage the capabilities of YOLOv6 models in a broad range of object detection scenarios.
## Citations and Acknowledgements
We would like to acknowledge the authors for their significant contributions in the field of real-time object detection:
!!! quote ""
=== "BibTeX"
```bibtex
@misc{li2023yolov6,
title={YOLOv6 v3.0: A Full-Scale Reloading},
author={Chuyi Li and Lulu Li and Yifei Geng and Hongliang Jiang and Meng Cheng and Bo Zhang and Zaidan Ke and Xiaoming Xu and Xiangxiang Chu},
year={2023},
eprint={2301.05586},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
The original YOLOv6 paper can be found on [arXiv](https://arxiv.org/abs/2301.05586). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/meituan/YOLOv6). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
## FAQ
### What is Meituan YOLOv6 and what makes it unique?
Meituan YOLOv6 is a state-of-the-art object detector that balances speed and accuracy, ideal for real-time applications. It features notable architectural enhancements like the Bi-directional Concatenation (BiC) module and an Anchor-Aided Training (AAT) strategy. These innovations provide substantial performance gains with minimal speed degradation, making YOLOv6 a competitive choice for object detection tasks.
### How does the Bi-directional Concatenation (BiC) Module in YOLOv6 improve performance?
The Bi-directional Concatenation (BiC) module in YOLOv6 enhances localization signals in the detector's neck, delivering performance improvements with negligible speed impact. This module effectively combines different feature maps, increasing the model's ability to detect objects accurately. For more details on YOLOv6's features, refer to the [Key Features](#key-features) section.
### How can I train a YOLOv6 model using Ultralytics?
You can train a YOLOv6 model using Ultralytics with simple Python or CLI commands. For instance:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Build a YOLOv6n model from scratch
model = YOLO("yolov6n.yaml")
# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
yolo train model=yolov6n.yaml data=coco8.yaml epochs=100 imgsz=640
```
For more information, visit the [Train](../modes/train.md) page.
### What are the different versions of YOLOv6 and their performance metrics?
YOLOv6 offers multiple versions, each optimized for different performance requirements:
- YOLOv6-N: 37.5% AP at 1187 FPS
- YOLOv6-S: 45.0% AP at 484 FPS
- YOLOv6-M: 50.0% AP at 226 FPS
- YOLOv6-L: 52.8% AP at 116 FPS
- YOLOv6-L6: State-of-the-art accuracy in real-time scenarios
These models are evaluated on the COCO dataset using an NVIDIA T4 GPU. For more on performance metrics, see the [Performance Metrics](#performance-metrics) section.
### How does the Anchor-Aided Training (AAT) strategy benefit YOLOv6?
Anchor-Aided Training (AAT) in YOLOv6 combines elements of anchor-based and anchor-free approaches, enhancing the model's detection capabilities without compromising inference efficiency. This strategy leverages anchors during training to improve [bounding box](https://www.ultralytics.com/glossary/bounding-box) predictions, making YOLOv6 effective in diverse object detection tasks.
### Which operational modes are supported by YOLOv6 models in Ultralytics?
YOLOv6 supports various operational modes including Inference, Validation, Training, and Export. This flexibility allows users to fully exploit the model's capabilities in different scenarios. Check out the [Supported Tasks and Modes](#supported-tasks-and-modes) section for a detailed overview of each mode.
docs/en/models/yolov7.md 0 → 100644
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---
comments: true
description: Discover YOLOv7, the breakthrough real-time object detector with top speed and accuracy. Learn about key features, usage, and performance metrics.
keywords: YOLOv7, real-time object detection, Ultralytics, AI, computer vision, model training, object detector
---
# YOLOv7: Trainable Bag-of-Freebies
YOLOv7 is a state-of-the-art real-time object detector that surpasses all known object detectors in both speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) in the range from 5 FPS to 160 FPS. It has the highest accuracy (56.8% AP) among all known real-time object detectors with 30 FPS or higher on GPU V100. Moreover, YOLOv7 outperforms other object detectors such as YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, and many others in speed and accuracy. The model is trained on the MS COCO dataset from scratch without using any other datasets or pre-trained weights. Source code for YOLOv7 is available on GitHub.
![YOLOv7 comparison with SOTA object detectors](https://github.com/ultralytics/docs/releases/download/0/yolov7-comparison-sota-object-detectors.avif)
## Comparison of SOTA object detectors
From the results in the YOLO comparison table we know that the proposed method has the best speed-accuracy trade-off comprehensively. If we compare YOLOv7-tiny-SiLU with YOLOv5-N (r6.1), our method is 127 fps faster and 10.7% more accurate on AP. In addition, YOLOv7 has 51.4% AP at frame rate of 161 fps, while PPYOLOE-L with the same AP has only 78 fps frame rate. In terms of parameter usage, YOLOv7 is 41% less than PPYOLOE-L. If we compare YOLOv7-X with 114 fps inference speed to YOLOv5-L (r6.1) with 99 fps inference speed, YOLOv7-X can improve AP by 3.9%. If YOLOv7-X is compared with YOLOv5-X (r6.1) of similar scale, the inference speed of YOLOv7-X is 31 fps faster. In addition, in terms the amount of parameters and computation, YOLOv7-X reduces 22% of parameters and 8% of computation compared to YOLOv5-X (r6.1), but improves AP by 2.2% ([Source](https://arxiv.org/pdf/2207.02696.pdf)).
| Model | Params<br><sup>(M) | FLOPs<br><sup>(G) | Size<br><sup>(pixels) | FPS | AP<sup>test / val<br>50-95 | AP<sup>test<br>50 | AP<sup>test<br>75 | AP<sup>test<br>S | AP<sup>test<br>M | AP<sup>test<br>L |
| --------------------- | ------------------ | ----------------- | --------------------- | ------- | -------------------------- | ----------------- | ----------------- | ---------------- | ---------------- | ---------------- |
| [YOLOX-S][1] | **9.0M** | **26.8G** | 640 | **102** | 40.5% / 40.5% | - | - | - | - | - |
| [YOLOX-M][1] | 25.3M | 73.8G | 640 | 81 | 47.2% / 46.9% | - | - | - | - | - |
| [YOLOX-L][1] | 54.2M | 155.6G | 640 | 69 | 50.1% / 49.7% | - | - | - | - | - |
| [YOLOX-X][1] | 99.1M | 281.9G | 640 | 58 | **51.5% / 51.1%** | - | - | - | - | - |
| | | | | | | | | | | |
| [PPYOLOE-S][2] | **7.9M** | **17.4G** | 640 | **208** | 43.1% / 42.7% | 60.5% | 46.6% | 23.2% | 46.4% | 56.9% |
| [PPYOLOE-M][2] | 23.4M | 49.9G | 640 | 123 | 48.9% / 48.6% | 66.5% | 53.0% | 28.6% | 52.9% | 63.8% |
| [PPYOLOE-L][2] | 52.2M | 110.1G | 640 | 78 | 51.4% / 50.9% | 68.9% | 55.6% | 31.4% | 55.3% | 66.1% |
| [PPYOLOE-X][2] | 98.4M | 206.6G | 640 | 45 | **52.2% / 51.9%** | **69.9%** | **56.5%** | **33.3%** | **56.3%** | **66.4%** |
| | | | | | | | | | | |
| [YOLOv5-N (r6.1)][3] | **1.9M** | **4.5G** | 640 | **159** | - / 28.0% | - | - | - | - | - |
| [YOLOv5-S (r6.1)][3] | 7.2M | 16.5G | 640 | 156 | - / 37.4% | - | - | - | - | - |
| [YOLOv5-M (r6.1)][3] | 21.2M | 49.0G | 640 | 122 | - / 45.4% | - | - | - | - | - |
| [YOLOv5-L (r6.1)][3] | 46.5M | 109.1G | 640 | 99 | - / 49.0% | - | - | - | - | - |
| [YOLOv5-X (r6.1)][3] | 86.7M | 205.7G | 640 | 83 | - / **50.7%** | - | - | - | - | - |
| | | | | | | | | | | |
| [YOLOR-CSP][4] | 52.9M | 120.4G | 640 | 106 | 51.1% / 50.8% | 69.6% | 55.7% | 31.7% | 55.3% | 64.7% |
| [YOLOR-CSP-X][4] | 96.9M | 226.8G | 640 | 87 | 53.0% / 52.7% | 71.4% | 57.9% | 33.7% | 57.1% | 66.8% |
| [YOLOv7-tiny-SiLU][5] | **6.2M** | **13.8G** | 640 | **286** | 38.7% / 38.7% | 56.7% | 41.7% | 18.8% | 42.4% | 51.9% |
| [YOLOv7][5] | 36.9M | 104.7G | 640 | 161 | 51.4% / 51.2% | 69.7% | 55.9% | 31.8% | 55.5% | 65.0% |
| [YOLOv7-X][5] | 71.3M | 189.9G | 640 | 114 | **53.1% / 52.9%** | **71.2%** | **57.8%** | **33.8%** | **57.1%** | **67.4%** |
| | | | | | | | | | | |
| [YOLOv5-N6 (r6.1)][3] | **3.2M** | **18.4G** | 1280 | **123** | - / 36.0% | - | - | - | - | - |
| [YOLOv5-S6 (r6.1)][3] | 12.6M | 67.2G | 1280 | 122 | - / 44.8% | - | - | - | - | - |
| [YOLOv5-M6 (r6.1)][3] | 35.7M | 200.0G | 1280 | 90 | - / 51.3% | - | - | - | - | - |
| [YOLOv5-L6 (r6.1)][3] | 76.8M | 445.6G | 1280 | 63 | - / 53.7% | - | - | - | - | - |
| [YOLOv5-X6 (r6.1)][3] | 140.7M | 839.2G | 1280 | 38 | - / **55.0%** | - | - | - | - | - |
| | | | | | | | | | | |
| [YOLOR-P6][4] | **37.2M** | **325.6G** | 1280 | **76** | 53.9% / 53.5% | 71.4% | 58.9% | 36.1% | 57.7% | 65.6% |
| [YOLOR-W6][4] | 79.8G | 453.2G | 1280 | 66 | 55.2% / 54.8% | 72.7% | 60.5% | 37.7% | 59.1% | 67.1% |
| [YOLOR-E6][4] | 115.8M | 683.2G | 1280 | 45 | 55.8% / 55.7% | 73.4% | 61.1% | 38.4% | 59.7% | 67.7% |
| [YOLOR-D6][4] | 151.7M | 935.6G | 1280 | 34 | **56.5% / 56.1%** | **74.1%** | **61.9%** | **38.9%** | **60.4%** | **68.7%** |
| | | | | | | | | | | |
| [YOLOv7-W6][5] | **70.4M** | **360.0G** | 1280 | **84** | 54.9% / 54.6% | 72.6% | 60.1% | 37.3% | 58.7% | 67.1% |
| [YOLOv7-E6][5] | 97.2M | 515.2G | 1280 | 56 | 56.0% / 55.9% | 73.5% | 61.2% | 38.0% | 59.9% | 68.4% |
| [YOLOv7-D6][5] | 154.7M | 806.8G | 1280 | 44 | 56.6% / 56.3% | 74.0% | 61.8% | 38.8% | 60.1% | 69.5% |
| [YOLOv7-E6E][5] | 151.7M | 843.2G | 1280 | 36 | **56.8% / 56.8%** | **74.4%** | **62.1%** | **39.3%** | **60.5%** | **69.0%** |
[1]: https://github.com/Megvii-BaseDetection/YOLOX
[2]: https://github.com/PaddlePaddle/PaddleDetection
[3]: https://github.com/ultralytics/yolov5
[4]: https://github.com/WongKinYiu/yolor
[5]: https://github.com/WongKinYiu/yolov7
## Overview
Real-time object detection is an important component in many [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) systems, including multi-object tracking, autonomous driving, robotics, and medical image analysis. In recent years, real-time object detection development has focused on designing efficient architectures and improving the inference speed of various CPUs, GPUs, and neural processing units (NPUs). YOLOv7 supports both mobile GPU and GPU devices, from the edge to the cloud.
Unlike traditional real-time object detectors that focus on architecture optimization, YOLOv7 introduces a focus on the optimization of the training process. This includes modules and optimization methods designed to improve the accuracy of object detection without increasing the inference cost, a concept known as the "trainable bag-of-freebies".
## Key Features
YOLOv7 introduces several key features:
1. **Model Re-parameterization**: YOLOv7 proposes a planned re-parameterized model, which is a strategy applicable to layers in different networks with the concept of gradient propagation path.
2. **Dynamic Label Assignment**: The training of the model with multiple output layers presents a new issue: "How to assign dynamic targets for the outputs of different branches?" To solve this problem, YOLOv7 introduces a new label assignment method called coarse-to-fine lead guided label assignment.
3. **Extended and Compound Scaling**: YOLOv7 proposes "extend" and "compound scaling" methods for the real-time object detector that can effectively utilize parameters and computation.
4. **Efficiency**: The method proposed by YOLOv7 can effectively reduce about 40% parameters and 50% computation of state-of-the-art real-time object detector, and has faster inference speed and higher detection accuracy.
## Usage Examples
As of the time of writing, Ultralytics does not currently support YOLOv7 models. Therefore, any users interested in using YOLOv7 will need to refer directly to the YOLOv7 GitHub repository for installation and usage instructions.
Here is a brief overview of the typical steps you might take to use YOLOv7:
1. Visit the YOLOv7 GitHub repository: [https://github.com/WongKinYiu/yolov7](https://github.com/WongKinYiu/yolov7).
2. Follow the instructions provided in the README file for installation. This typically involves cloning the repository, installing necessary dependencies, and setting up any necessary environment variables.
3. Once installation is complete, you can train and use the model as per the usage instructions provided in the repository. This usually involves preparing your dataset, configuring the model parameters, training the model, and then using the trained model to perform object detection.
Please note that the specific steps may vary depending on your specific use case and the current state of the YOLOv7 repository. Therefore, it is strongly recommended to refer directly to the instructions provided in the YOLOv7 GitHub repository.
We regret any inconvenience this may cause and will strive to update this document with usage examples for Ultralytics once support for YOLOv7 is implemented.
## Citations and Acknowledgements
We would like to acknowledge the YOLOv7 authors for their significant contributions in the field of real-time object detection:
!!! quote ""
=== "BibTeX"
```bibtex
@article{wang2022yolov7,
title={YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
journal={arXiv preprint arXiv:2207.02696},
year={2022}
}
```
The original YOLOv7 paper can be found on [arXiv](https://arxiv.org/pdf/2207.02696.pdf). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/WongKinYiu/yolov7). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
## FAQ
### What is YOLOv7 and why is it considered a breakthrough in real-time [object detection](https://www.ultralytics.com/glossary/object-detection)?
YOLOv7 is a cutting-edge real-time object detection model that achieves unparalleled speed and accuracy. It surpasses other models, such as YOLOX, YOLOv5, and PPYOLOE, in both parameters usage and inference speed. YOLOv7's distinguishing features include its model re-parameterization and dynamic label assignment, which optimize its performance without increasing inference costs. For more technical details about its architecture and comparison metrics with other state-of-the-art object detectors, refer to the [YOLOv7 paper](https://arxiv.org/pdf/2207.02696.pdf).
### How does YOLOv7 improve on previous YOLO models like YOLOv4 and YOLOv5?
YOLOv7 introduces several innovations, including model re-parameterization and dynamic label assignment, which enhance the training process and improve inference accuracy. Compared to YOLOv5, YOLOv7 significantly boosts speed and accuracy. For instance, YOLOv7-X improves accuracy by 2.2% and reduces parameters by 22% compared to YOLOv5-X. Detailed comparisons can be found in the performance table [YOLOv7 comparison with SOTA object detectors](#comparison-of-sota-object-detectors).
### Can I use YOLOv7 with Ultralytics tools and platforms?
As of now, Ultralytics does not directly support YOLOv7 in its tools and platforms. Users interested in using YOLOv7 need to follow the installation and usage instructions provided in the [YOLOv7 GitHub repository](https://github.com/WongKinYiu/yolov7). For other state-of-the-art models, you can explore and train using Ultralytics tools like [Ultralytics HUB](../hub/quickstart.md).
### How do I install and run YOLOv7 for a custom object detection project?
To install and run YOLOv7, follow these steps:
1. Clone the YOLOv7 repository:
```bash
git clone https://github.com/WongKinYiu/yolov7
```
2. Navigate to the cloned directory and install dependencies:
```bash
cd yolov7
pip install -r requirements.txt
```
3. Prepare your dataset and configure the model parameters according to the [usage instructions](https://github.com/WongKinYiu/yolov7) provided in the repository.
For further guidance, visit the YOLOv7 GitHub repository for the latest information and updates.
### What are the key features and optimizations introduced in YOLOv7?
YOLOv7 offers several key features that revolutionize real-time object detection:
- **Model Re-parameterization**: Enhances the model's performance by optimizing gradient propagation paths.
- **Dynamic Label Assignment**: Uses a coarse-to-fine lead guided method to assign dynamic targets for outputs across different branches, improving accuracy.
- **Extended and Compound Scaling**: Efficiently utilizes parameters and computation to scale the model for various real-time applications.
- **Efficiency**: Reduces parameter count by 40% and computation by 50% compared to other state-of-the-art models while achieving faster inference speeds.
For further details on these features, see the [YOLOv7 Overview](#overview) section.
docs/en/models/yolov8.md 0 → 100644
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---
comments: true
description: Discover YOLOv8, the latest advancement in real-time object detection, optimizing performance with an array of pre-trained models for diverse tasks.
keywords: YOLOv8, real-time object detection, YOLO series, Ultralytics, computer vision, advanced object detection, AI, machine learning, deep learning
---
# Ultralytics YOLOv8
## Overview
YOLOv8 is the latest iteration in the YOLO series of real-time object detectors, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduces new features and optimizations that make it an ideal choice for various [object detection](https://www.ultralytics.com/glossary/object-detection) tasks in a wide range of applications.
![Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/yolov8-comparison-plots.avif)
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Na0HvJ4hkk0"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Ultralytics YOLOv8 Model Overview
</p>
## Key Features
- **Advanced Backbone and Neck Architectures:** YOLOv8 employs state-of-the-art backbone and neck architectures, resulting in improved [feature extraction](https://www.ultralytics.com/glossary/feature-extraction) and object detection performance.
- **Anchor-free Split Ultralytics Head:** YOLOv8 adopts an anchor-free split Ultralytics head, which contributes to better accuracy and a more efficient detection process compared to anchor-based approaches.
- **Optimized Accuracy-Speed Tradeoff:** With a focus on maintaining an optimal balance between accuracy and speed, YOLOv8 is suitable for real-time object detection tasks in diverse application areas.
- **Variety of Pre-trained Models:** YOLOv8 offers a range of pre-trained models to cater to various tasks and performance requirements, making it easier to find the right model for your specific use case.
## Supported Tasks and Modes
The YOLOv8 series offers a diverse range of models, each specialized for specific tasks in computer vision. These models are designed to cater to various requirements, from object detection to more complex tasks like [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), pose/keypoints detection, oriented object detection, and classification.
Each variant of the YOLOv8 series is optimized for its respective task, ensuring high performance and accuracy. Additionally, these models are compatible with various operational modes including [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), facilitating their use in different stages of deployment and development.
| Model | Filenames | Task | Inference | Validation | Training | Export |
| ----------- | -------------------------------------------------------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv8 | `yolov8n.pt` `yolov8s.pt` `yolov8m.pt` `yolov8l.pt` `yolov8x.pt` | [Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-seg | `yolov8n-seg.pt` `yolov8s-seg.pt` `yolov8m-seg.pt` `yolov8l-seg.pt` `yolov8x-seg.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-pose | `yolov8n-pose.pt` `yolov8s-pose.pt` `yolov8m-pose.pt` `yolov8l-pose.pt` `yolov8x-pose.pt` `yolov8x-pose-p6.pt` | [Pose/Keypoints](../tasks/pose.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-obb | `yolov8n-obb.pt` `yolov8s-obb.pt` `yolov8m-obb.pt` `yolov8l-obb.pt` `yolov8x-obb.pt` | [Oriented Detection](../tasks/obb.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8-cls | `yolov8n-cls.pt` `yolov8s-cls.pt` `yolov8m-cls.pt` `yolov8l-cls.pt` `yolov8x-cls.pt` | [Classification](../tasks/classify.md) | ✅ | ✅ | ✅ | ✅ |
This table provides an overview of the YOLOv8 model variants, highlighting their applicability in specific tasks and their compatibility with various operational modes such as Inference, Validation, Training, and Export. It showcases the versatility and robustness of the YOLOv8 series, making them suitable for a variety of applications in [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
## Performance Metrics
!!! performance
=== "Detection (COCO)"
See [Detection Docs](../tasks/detect.md) for usage examples with these models trained on [COCO](../datasets/detect/coco.md), which include 80 pre-trained classes.
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
=== "Detection (Open Images V7)"
See [Detection Docs](../tasks/detect.md) for usage examples with these models trained on [Open Image V7](../datasets/detect/open-images-v7.md), which include 600 pre-trained classes.
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ----------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
=== "Segmentation (COCO)"
See [Segmentation Docs](../tasks/segment.md) for usage examples with these models trained on [COCO](../datasets/segment/coco.md), which include 80 pre-trained classes.
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
=== "Classification (ImageNet)"
See [Classification Docs](../tasks/classify.md) for usage examples with these models trained on [ImageNet](../datasets/classify/imagenet.md), which include 1000 pre-trained classes.
| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-cls.pt) | 224 | 69.0 | 88.3 | 12.9 | 0.31 | 2.7 | 4.3 |
| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-cls.pt) | 224 | 73.8 | 91.7 | 23.4 | 0.35 | 6.4 | 13.5 |
| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-cls.pt) | 224 | 76.8 | 93.5 | 85.4 | 0.62 | 17.0 | 42.7 |
| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-cls.pt) | 224 | 76.8 | 93.5 | 163.0 | 0.87 | 37.5 | 99.7 |
| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-cls.pt) | 224 | 79.0 | 94.6 | 232.0 | 1.01 | 57.4 | 154.8 |
=== "Pose (COCO)"
See [Pose Estimation Docs](../tasks/pose.md) for usage examples with these models trained on [COCO](../datasets/pose/coco.md), which include 1 pre-trained class, 'person'.
| Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-pose.pt) | 640 | 50.4 | 80.1 | 131.8 | 1.18 | 3.3 | 9.2 |
| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-pose.pt) | 640 | 60.0 | 86.2 | 233.2 | 1.42 | 11.6 | 30.2 |
| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-pose.pt) | 640 | 65.0 | 88.8 | 456.3 | 2.00 | 26.4 | 81.0 |
| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-pose.pt) | 640 | 67.6 | 90.0 | 784.5 | 2.59 | 44.4 | 168.6 |
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 |
=== "OBB (DOTAv1)"
See [Oriented Detection Docs](../tasks/obb.md) for usage examples with these models trained on [DOTAv1](../datasets/obb/dota-v2.md#dota-v10), which include 15 pre-trained classes.
| Model | size<br><sup>(pixels) | mAP<sup>test<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|----------------------------------------------------------------------------------------------|-----------------------| -------------------- | -------------------------------- | ------------------------------------- | -------------------- | ----------------- |
| [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-obb.pt) | 1024 | 78.0 | 204.77 | 3.57 | 3.1 | 23.3 |
| [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-obb.pt) | 1024 | 79.5 | 424.88 | 4.07 | 11.4 | 76.3 |
| [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-obb.pt) | 1024 | 80.5 | 763.48 | 7.61 | 26.4 | 208.6 |
| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-obb.pt) | 1024 | 80.7 | 1278.42 | 11.83 | 44.5 | 433.8 |
| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-obb.pt) | 1024 | 81.36 | 1759.10 | 13.23 | 69.5 | 676.7 |
## Usage Examples
This example provides simple YOLOv8 training and inference examples. For full documentation on these and other [modes](../modes/index.md) see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md) docs pages.
Note the below example is for YOLOv8 [Detect](../tasks/detect.md) models for object detection. For additional supported tasks see the [Segment](../tasks/segment.md), [Classify](../tasks/classify.md), [OBB](../tasks/obb.md) docs and [Pose](../tasks/pose.md) docs.
!!! example
=== "Python"
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
```python
from ultralytics import YOLO
# Load a COCO-pretrained YOLOv8n model
model = YOLO("yolov8n.pt")
# Display model information (optional)
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
# Run inference with the YOLOv8n model on the 'bus.jpg' image
results = model("path/to/bus.jpg")
```
=== "CLI"
CLI commands are available to directly run the models:
```bash
# Load a COCO-pretrained YOLOv8n model and train it on the COCO8 example dataset for 100 epochs
yolo train model=yolov8n.pt data=coco8.yaml epochs=100 imgsz=640
# Load a COCO-pretrained YOLOv8n model and run inference on the 'bus.jpg' image
yolo predict model=yolov8n.pt source=path/to/bus.jpg
```
## Citations and Acknowledgements
!!! tip "Ultralytics YOLOv8 Publication"
Ultralytics has not published a formal research paper for YOLOv8 due to the rapidly evolving nature of the models. We focus on advancing the technology and making it easier to use, rather than producing static documentation. For the most up-to-date information on YOLO architecture, features, and usage, please refer to our [GitHub repository](https://github.com/ultralytics/ultralytics) and [documentation](https://docs.ultralytics.com/).
If you use the YOLOv8 model or any other software from this repository in your work, please cite it using the following format:
!!! quote ""
=== "BibTeX"
```bibtex
@software{yolov8_ultralytics,
author = {Glenn Jocher and Ayush Chaurasia and Jing Qiu},
title = {Ultralytics YOLOv8},
version = {8.0.0},
year = {2023},
url = {https://github.com/ultralytics/ultralytics},
orcid = {0000-0001-5950-6979, 0000-0002-7603-6750, 0000-0003-3783-7069},
license = {AGPL-3.0}
}
```
Please note that the DOI is pending and will be added to the citation once it is available. YOLOv8 models are provided under [AGPL-3.0](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) and [Enterprise](https://www.ultralytics.com/license) licenses.
## FAQ
### What is YOLOv8 and how does it differ from previous YOLO versions?
YOLOv8 is the latest iteration in the Ultralytics YOLO series, designed to improve real-time object detection performance with advanced features. Unlike earlier versions, YOLOv8 incorporates an **anchor-free split Ultralytics head**, state-of-the-art backbone and neck architectures, and offers optimized [accuracy](https://www.ultralytics.com/glossary/accuracy)-speed tradeoff, making it ideal for diverse applications. For more details, check the [Overview](#overview) and [Key Features](#key-features) sections.
### How can I use YOLOv8 for different computer vision tasks?
YOLOv8 supports a wide range of computer vision tasks, including object detection, instance segmentation, pose/keypoints detection, oriented object detection, and classification. Each model variant is optimized for its specific task and compatible with various operational modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). Refer to the [Supported Tasks and Modes](#supported-tasks-and-modes) section for more information.
### What are the performance metrics for YOLOv8 models?
YOLOv8 models achieve state-of-the-art performance across various benchmarking datasets. For instance, the YOLOv8n model achieves a mAP (mean Average Precision) of 37.3 on the COCO dataset and a speed of 0.99 ms on A100 TensorRT. Detailed performance metrics for each model variant across different tasks and datasets can be found in the [Performance Metrics](#performance-metrics) section.
### How do I train a YOLOv8 model?
Training a YOLOv8 model can be done using either Python or CLI. Below are examples for training a model using a COCO-pretrained YOLOv8 model on the COCO8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch):
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a COCO-pretrained YOLOv8n model
model = YOLO("yolov8n.pt")
# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
yolo train model=yolov8n.pt data=coco8.yaml epochs=100 imgsz=640
```
For further details, visit the [Training](../modes/train.md) documentation.
### Can I benchmark YOLOv8 models for performance?
Yes, YOLOv8 models can be benchmarked for performance in terms of speed and accuracy across various export formats. You can use PyTorch, ONNX, TensorRT, and more for benchmarking. Below are example commands for benchmarking using Python and CLI:
!!! example
=== "Python"
```python
from ultralytics.utils.benchmarks import benchmark
# Benchmark on GPU
benchmark(model="yolov8n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
```
=== "CLI"
```bash
yolo benchmark model=yolov8n.pt data='coco8.yaml' imgsz=640 half=False device=0
```
For additional information, check the [Performance Metrics](#performance-metrics) section.
docs/en/models/yolov9.md 0 → 100644
View file @ af155c51
---
comments: true
description: Explore YOLOv9, the latest leap in real-time object detection, featuring innovations like PGI and GELAN, and achieving new benchmarks in efficiency and accuracy.
keywords: YOLOv9, object detection, real-time, PGI, GELAN, deep learning, MS COCO, AI, neural networks, model efficiency, accuracy, Ultralytics
---
# YOLOv9: A Leap Forward in [Object Detection](https://www.ultralytics.com/glossary/object-detection) Technology
YOLOv9 marks a significant advancement in real-time object detection, introducing groundbreaking techniques such as Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN). This model demonstrates remarkable improvements in efficiency, accuracy, and adaptability, setting new benchmarks on the MS COCO dataset. The YOLOv9 project, while developed by a separate open-source team, builds upon the robust codebase provided by [Ultralytics](https://www.ultralytics.com/) [YOLOv5](yolov5.md), showcasing the collaborative spirit of the AI research community.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/ZF7EAodHn1U"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> YOLOv9 Training on Custom Data using Ultralytics | Industrial Package Dataset
</p>
![YOLOv9 performance comparison](https://github.com/ultralytics/docs/releases/download/0/yolov9-performance-comparison.avif)
## Introduction to YOLOv9
In the quest for optimal real-time object detection, YOLOv9 stands out with its innovative approach to overcoming information loss challenges inherent in deep [neural networks](https://www.ultralytics.com/glossary/neural-network-nn). By integrating PGI and the versatile GELAN architecture, YOLOv9 not only enhances the model's learning capacity but also ensures the retention of crucial information throughout the detection process, thereby achieving exceptional accuracy and performance.
## Core Innovations of YOLOv9
YOLOv9's advancements are deeply rooted in addressing the challenges posed by information loss in deep neural networks. The Information Bottleneck Principle and the innovative use of Reversible Functions are central to its design, ensuring YOLOv9 maintains high efficiency and accuracy.
### Information Bottleneck Principle
The Information Bottleneck Principle reveals a fundamental challenge in deep learning: as data passes through successive layers of a network, the potential for information loss increases. This phenomenon is mathematically represented as:
```python
I(X, X) >= I(X, f_theta(X)) >= I(X, g_phi(f_theta(X)))
```
where `I` denotes mutual information, and `f` and `g` represent transformation functions with parameters `theta` and `phi`, respectively. YOLOv9 counters this challenge by implementing Programmable Gradient Information (PGI), which aids in preserving essential data across the network's depth, ensuring more reliable gradient generation and, consequently, better model convergence and performance.
### Reversible Functions
The concept of Reversible Functions is another cornerstone of YOLOv9's design. A function is deemed reversible if it can be inverted without any loss of information, as expressed by:
```python
X = v_zeta(r_psi(X))
```
with `psi` and `zeta` as parameters for the reversible and its inverse function, respectively. This property is crucial for [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) architectures, as it allows the network to retain a complete information flow, thereby enabling more accurate updates to the model's parameters. YOLOv9 incorporates reversible functions within its architecture to mitigate the risk of information degradation, especially in deeper layers, ensuring the preservation of critical data for object detection tasks.
### Impact on Lightweight Models
Addressing information loss is particularly vital for lightweight models, which are often under-parameterized and prone to losing significant information during the feedforward process. YOLOv9's architecture, through the use of PGI and reversible functions, ensures that even with a streamlined model, the essential information required for accurate object detection is retained and effectively utilized.
### Programmable Gradient Information (PGI)
PGI is a novel concept introduced in YOLOv9 to combat the information bottleneck problem, ensuring the preservation of essential data across deep network layers. This allows for the generation of reliable gradients, facilitating accurate model updates and improving the overall detection performance.
### Generalized Efficient Layer Aggregation Network (GELAN)
GELAN represents a strategic architectural advancement, enabling YOLOv9 to achieve superior parameter utilization and computational efficiency. Its design allows for flexible integration of various computational blocks, making YOLOv9 adaptable to a wide range of applications without sacrificing speed or accuracy.
![YOLOv9 architecture comparison](https://github.com/ultralytics/docs/releases/download/0/yolov9-architecture-comparison.avif)
## YOLOv9 Benchmarks
Benchmarking in YOLOv9 using [Ultralytics](https://docs.ultralytics.com/modes/benchmark/) involves evaluating the performance of your trained and validated model in real-world scenarios. This process includes:
- **Performance Evaluation:** Assessing the model's speed and accuracy.
- **Export Formats:** Testing the model across different export formats to ensure it meets the necessary standards and performs well in various environments.
- **Framework Support:** Providing a comprehensive framework within Ultralytics YOLOv8 to facilitate these assessments and ensure consistent and reliable results.
By benchmarking, you can ensure that your model not only performs well in controlled testing environments but also maintains high performance in practical, real-world applications.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/ziJR01lKnio"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How to Benchmark the YOLOv9 Model Using the Ultralytics Python Package
</p>
## Performance on MS COCO Dataset
The performance of YOLOv9 on the [COCO dataset](../datasets/detect/coco.md) exemplifies its significant advancements in real-time object detection, setting new benchmarks across various model sizes. Table 1 presents a comprehensive comparison of state-of-the-art real-time object detectors, illustrating YOLOv9's superior efficiency and [accuracy](https://www.ultralytics.com/glossary/accuracy).
**Table 1. Comparison of State-of-the-Art Real-Time Object Detectors**
!!! tip "Performance"
=== "Detection (COCO)"
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | params<br><sup>(M) | FLOPs<br><sup>(B) |
|---------------------------------------------------------------------------------------|-----------------------|----------------------|-------------------|--------------------|-------------------|
| [YOLOv9t](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9t.pt) | 640 | 38.3 | 53.1 | 2.0 | 7.7 |
| [YOLOv9s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9s.pt) | 640 | 46.8 | 63.4 | 7.2 | 26.7 |
| [YOLOv9m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9m.pt) | 640 | 51.4 | 68.1 | 20.1 | 76.8 |
| [YOLOv9c](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9c.pt) | 640 | 53.0 | 70.2 | 25.5 | 102.8 |
| [YOLOv9e](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9e.pt) | 640 | 55.6 | 72.8 | 58.1 | 192.5 |
=== "Segmentation (COCO)"
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | params<br><sup>(M) | FLOPs<br><sup>(B) |
|-----------------------------------------------------------------------------------------------|-----------------------|----------------------|-----------------------|--------------------|-------------------|
| [YOLOv9c-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9c-seg.pt) | 640 | 52.4 | 42.2 | 27.9 | 159.4 |
| [YOLOv9e-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9e-seg.pt) | 640 | 55.1 | 44.3 | 60.5 | 248.4 |
YOLOv9's iterations, ranging from the tiny `t` variant to the extensive `e` model, demonstrate improvements not only in accuracy (mAP metrics) but also in efficiency with a reduced number of parameters and computational needs (FLOPs). This table underscores YOLOv9's ability to deliver high [precision](https://www.ultralytics.com/glossary/precision) while maintaining or reducing the computational overhead compared to prior versions and competing models.
Comparatively, YOLOv9 exhibits remarkable gains:
- **Lightweight Models**: YOLOv9s surpasses the YOLO MS-S in parameter efficiency and computational load while achieving an improvement of 0.4∼0.6% in AP.
- **Medium to Large Models**: YOLOv9m and YOLOv9e show notable advancements in balancing the trade-off between model complexity and detection performance, offering significant reductions in parameters and computations against the backdrop of improved accuracy.
The YOLOv9c model, in particular, highlights the effectiveness of the architecture's optimizations. It operates with 42% fewer parameters and 21% less computational demand than YOLOv7 AF, yet it achieves comparable accuracy, demonstrating YOLOv9's significant efficiency improvements. Furthermore, the YOLOv9e model sets a new standard for large models, with 15% fewer parameters and 25% less computational need than [YOLOv8x](yolov8.md), alongside an incremental 1.7% improvement in AP.
These results showcase YOLOv9's strategic advancements in model design, emphasizing its enhanced efficiency without compromising on the precision essential for real-time object detection tasks. The model not only pushes the boundaries of performance metrics but also emphasizes the importance of computational efficiency, making it a pivotal development in the field of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
## Conclusion
YOLOv9 represents a pivotal development in real-time object detection, offering significant improvements in terms of efficiency, accuracy, and adaptability. By addressing critical challenges through innovative solutions like PGI and GELAN, YOLOv9 sets a new precedent for future research and application in the field. As the AI community continues to evolve, YOLOv9 stands as a testament to the power of collaboration and innovation in driving technological progress.
## Usage Examples
This example provides simple YOLOv9 training and inference examples. For full documentation on these and other [modes](../modes/index.md) see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md) docs pages.
!!! example
=== "Python"
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
```python
from ultralytics import YOLO
# Build a YOLOv9c model from scratch
model = YOLO("yolov9c.yaml")
# Build a YOLOv9c model from pretrained weight
model = YOLO("yolov9c.pt")
# Display model information (optional)
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
# Run inference with the YOLOv9c model on the 'bus.jpg' image
results = model("path/to/bus.jpg")
```
=== "CLI"
CLI commands are available to directly run the models:
```bash
# Build a YOLOv9c model from scratch and train it on the COCO8 example dataset for 100 epochs
yolo train model=yolov9c.yaml data=coco8.yaml epochs=100 imgsz=640
# Build a YOLOv9c model from scratch and run inference on the 'bus.jpg' image
yolo predict model=yolov9c.yaml source=path/to/bus.jpg
```
## Supported Tasks and Modes
The YOLOv9 series offers a range of models, each optimized for high-performance [Object Detection](../tasks/detect.md). These models cater to varying computational needs and accuracy requirements, making them versatile for a wide array of applications.
| Model | Filenames | Tasks | Inference | Validation | Training | Export |
| ---------- | ------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv9 | `yolov9t` `yolov9s` `yolov9m` `yolov9c.pt` `yolov9e.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv9-seg | `yolov9c-seg.pt` `yolov9e-seg.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ |
This table provides a detailed overview of the YOLOv9 model variants, highlighting their capabilities in object detection tasks and their compatibility with various operational modes such as [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This comprehensive support ensures that users can fully leverage the capabilities of YOLOv9 models in a broad range of object detection scenarios.
!!! note
Training YOLOv9 models will require _more_ resources **and** take longer than the equivalent sized [YOLOv8 model](yolov8.md).
## Citations and Acknowledgements
We would like to acknowledge the YOLOv9 authors for their significant contributions in the field of real-time object detection:
!!! quote ""
=== "BibTeX"
```bibtex
@article{wang2024yolov9,
title={YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information},
author={Wang, Chien-Yao and Liao, Hong-Yuan Mark},
booktitle={arXiv preprint arXiv:2402.13616},
year={2024}
}
```
The original YOLOv9 paper can be found on [arXiv](https://arxiv.org/pdf/2402.13616.pdf). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/WongKinYiu/yolov9). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
## FAQ
### What innovations does YOLOv9 introduce for real-time object detection?
YOLOv9 introduces groundbreaking techniques such as Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN). These innovations address information loss challenges in deep neural networks, ensuring high efficiency, accuracy, and adaptability. PGI preserves essential data across network layers, while GELAN optimizes parameter utilization and computational efficiency. Learn more about [YOLOv9's core innovations](#core-innovations-of-yolov9) that set new benchmarks on the MS COCO dataset.
### How does YOLOv9 perform on the MS COCO dataset compared to other models?
YOLOv9 outperforms state-of-the-art real-time object detectors by achieving higher accuracy and efficiency. On the [COCO dataset](../datasets/detect/coco.md), YOLOv9 models exhibit superior mAP scores across various sizes while maintaining or reducing computational overhead. For instance, YOLOv9c achieves comparable accuracy with 42% fewer parameters and 21% less computational demand than YOLOv7 AF. Explore [performance comparisons](#performance-on-ms-coco-dataset) for detailed metrics.
### How can I train a YOLOv9 model using Python and CLI?
You can train a YOLOv9 model using both Python and CLI commands. For Python, instantiate a model using the `YOLO` class and call the `train` method:
```python
from ultralytics import YOLO
# Build a YOLOv9c model from pretrained weights and train
model = YOLO("yolov9c.pt")
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
```
For CLI training, execute:
```bash
yolo train model=yolov9c.yaml data=coco8.yaml epochs=100 imgsz=640
```
Learn more about [usage examples](#usage-examples) for training and inference.
### What are the advantages of using Ultralytics YOLOv9 for lightweight models?
YOLOv9 is designed to mitigate information loss, which is particularly important for lightweight models often prone to losing significant information. By integrating Programmable Gradient Information (PGI) and reversible functions, YOLOv9 ensures essential data retention, enhancing the model's accuracy and efficiency. This makes it highly suitable for applications requiring compact models with high performance. For more details, explore the section on [YOLOv9's impact on lightweight models](#impact-on-lightweight-models).
### What tasks and modes does YOLOv9 support?
YOLOv9 supports various tasks including object detection and [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation). It is compatible with multiple operational modes such as inference, validation, training, and export. This versatility makes YOLOv9 adaptable to diverse real-time computer vision applications. Refer to the [supported tasks and modes](#supported-tasks-and-modes) section for more information.
docs/en/modes/benchmark.md 0 → 100644
View file @ af155c51
---
comments: true
description: Learn how to evaluate your YOLO11 model's performance in real-world scenarios using benchmark mode. Optimize speed, accuracy, and resource allocation across export formats.
keywords: model benchmarking, YOLO11, Ultralytics, performance evaluation, export formats, ONNX, TensorRT, OpenVINO, CoreML, TensorFlow, optimization, mAP50-95, inference time
---
<script>
const script = document.createElement('script');
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document.head.appendChild(script);
const anotherScript = document.createElement('script');
anotherScript.src = "../../javascript/benchmark.js";
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# Model Benchmarking with Ultralytics YOLO
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif" alt="Ultralytics YOLO ecosystem and integrations">
## Benchmark Visualization
!!! tip "Refresh Browser"
You may need to refresh the page to view the graphs correctly due to potential cookie issues.
<div style="display: flex; align-items: flex-start;">
<div style="margin-right: 20px;">
<label><input type="checkbox" name="algorithm" value="YOLO11" checked><span>YOLO11</span></label><br>
<label><input type="checkbox" name="algorithm" value="YOLOv10" checked><span>YOLOv10</span></label><br>
<label><input type="checkbox" name="algorithm" value="YOLOv9" checked><span>YOLOv9</span></label><br>
<label><input type="checkbox" name="algorithm" value="YOLOv8" checked><span>YOLOv8</span></label><br>
<label><input type="checkbox" name="algorithm" value="YOLOv7" checked><span>YOLOv7</span></label><br>
<label><input type="checkbox" name="algorithm" value="YOLOv6-3.0" checked><span>YOLOv6-3.0</span></label><br>
<label><input type="checkbox" name="algorithm" value="YOLOv5" checked><span>YOLOv5</span></label><br>
<label><input type="checkbox" name="algorithm" value="PP-YOLOE+" checked><span>PP-YOLOE+</span></label><br>
<label><input type="checkbox" name="algorithm" value="DAMO-YOLO" checked><span>DAMO-YOLO</span></label><br>
<label><input type="checkbox" name="algorithm" value="YOLOX" checked><span>YOLOX</span></label><br>
<label><input type="checkbox" name="algorithm" value="RTDETRv2" checked><span>RTDETRv2</span></label>
</div>
<div style="flex-grow: 1;"><canvas id="chart"></canvas></div>
</div>
## Introduction
Once your model is trained and validated, the next logical step is to evaluate its performance in various real-world scenarios. Benchmark mode in Ultralytics YOLO11 serves this purpose by providing a robust framework for assessing the speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) of your model across a range of export formats.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/rEQlAaevEFc"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Benchmark Ultralytics YOLO11 Models | How to Compare Model Performance on Different Hardware?
</p>
## Why Is Benchmarking Crucial?
- **Informed Decisions:** Gain insights into the trade-offs between speed and accuracy.
- **Resource Allocation:** Understand how different export formats perform on different hardware.
- **Optimization:** Learn which export format offers the best performance for your specific use case.
- **Cost Efficiency:** Make more efficient use of hardware resources based on benchmark results.
### Key Metrics in Benchmark Mode
- **mAP50-95:** For [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and pose estimation.
- **accuracy_top5:** For [image classification](https://www.ultralytics.com/glossary/image-classification).
- **Inference Time:** Time taken for each image in milliseconds.
### Supported Export Formats
- **ONNX:** For optimal CPU performance
- **TensorRT:** For maximal GPU efficiency
- **OpenVINO:** For Intel hardware optimization
- **CoreML, TensorFlow SavedModel, and More:** For diverse deployment needs.
!!! tip
* Export to ONNX or OpenVINO for up to 3x CPU speedup.
* Export to TensorRT for up to 5x GPU speedup.
## Usage Examples
Run YOLO11n benchmarks on all supported export formats including ONNX, TensorRT etc. See Arguments section below for a full list of export arguments.
!!! example
=== "Python"
```python
from ultralytics.utils.benchmarks import benchmark
# Benchmark on GPU
benchmark(model="yolo11n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
```
=== "CLI"
```bash
yolo benchmark model=yolo11n.pt data='coco8.yaml' imgsz=640 half=False device=0
```
## Arguments
Arguments such as `model`, `data`, `imgsz`, `half`, `device`, and `verbose` provide users with the flexibility to fine-tune the benchmarks to their specific needs and compare the performance of different export formats with ease.
| Key | Default Value | Description |
| --------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model` | `None` | Specifies the path to the model file. Accepts both `.pt` and `.yaml` formats, e.g., `"yolo11n.pt"` for pre-trained models or configuration files. |
| `data` | `None` | Path to a YAML file defining the dataset for benchmarking, typically including paths and settings for [validation data](https://www.ultralytics.com/glossary/validation-data). Example: `"coco8.yaml"`. |
| `imgsz` | `640` | The input image size for the model. Can be a single integer for square images or a tuple `(width, height)` for non-square, e.g., `(640, 480)`. |
| `half` | `False` | Enables FP16 (half-precision) inference, reducing memory usage and possibly increasing speed on compatible hardware. Use `half=True` to enable. |
| `int8` | `False` | Activates INT8 quantization for further optimized performance on supported devices, especially useful for edge devices. Set `int8=True` to use. |
| `device` | `None` | Defines the computation device(s) for benchmarking, such as `"cpu"` or `"cuda:0"`. |
| `verbose` | `False` | Controls the level of detail in logging output. A boolean value; set `verbose=True` for detailed logs or a float for thresholding errors. |
## Export Formats
Benchmarks will attempt to run automatically on all possible export formats below.
{% include "macros/export-table.md" %}
See full `export` details in the [Export](../modes/export.md) page.
## FAQ
### How do I benchmark my YOLO11 model's performance using Ultralytics?
Ultralytics YOLO11 offers a Benchmark mode to assess your model's performance across different export formats. This mode provides insights into key metrics such as [mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP50-95), accuracy, and inference time in milliseconds. To run benchmarks, you can use either Python or CLI commands. For example, to benchmark on a GPU:
!!! example
=== "Python"
```python
from ultralytics.utils.benchmarks import benchmark
# Benchmark on GPU
benchmark(model="yolo11n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
```
=== "CLI"
```bash
yolo benchmark model=yolo11n.pt data='coco8.yaml' imgsz=640 half=False device=0
```
For more details on benchmark arguments, visit the [Arguments](#arguments) section.
### What are the benefits of exporting YOLO11 models to different formats?
Exporting YOLO11 models to different formats such as ONNX, TensorRT, and OpenVINO allows you to optimize performance based on your deployment environment. For instance:
- **ONNX:** Provides up to 3x CPU speedup.
- **TensorRT:** Offers up to 5x GPU speedup.
- **OpenVINO:** Specifically optimized for Intel hardware.
These formats enhance both the speed and accuracy of your models, making them more efficient for various real-world applications. Visit the [Export](../modes/export.md) page for complete details.
### Why is benchmarking crucial in evaluating YOLO11 models?
Benchmarking your YOLO11 models is essential for several reasons:
- **Informed Decisions:** Understand the trade-offs between speed and accuracy.
- **Resource Allocation:** Gauge the performance across different hardware options.
- **Optimization:** Determine which export format offers the best performance for specific use cases.
- **Cost Efficiency:** Optimize hardware usage based on benchmark results.
Key metrics such as mAP50-95, Top-5 accuracy, and inference time help in making these evaluations. Refer to the [Key Metrics](#key-metrics-in-benchmark-mode) section for more information.
### Which export formats are supported by YOLO11, and what are their advantages?
YOLO11 supports a variety of export formats, each tailored for specific hardware and use cases:
- **ONNX:** Best for CPU performance.
- **TensorRT:** Ideal for GPU efficiency.
- **OpenVINO:** Optimized for Intel hardware.
- **CoreML & [TensorFlow](https://www.ultralytics.com/glossary/tensorflow):** Useful for iOS and general ML applications.
For a complete list of supported formats and their respective advantages, check out the [Supported Export Formats](#supported-export-formats) section.
### What arguments can I use to fine-tune my YOLO11 benchmarks?
When running benchmarks, several arguments can be customized to suit specific needs:
- **model:** Path to the model file (e.g., "yolo11n.pt").
- **data:** Path to a YAML file defining the dataset (e.g., "coco8.yaml").
- **imgsz:** The input image size, either as a single integer or a tuple.
- **half:** Enable FP16 inference for better performance.
- **int8:** Activate INT8 quantization for edge devices.
- **device:** Specify the computation device (e.g., "cpu", "cuda:0").
- **verbose:** Control the level of logging detail.
For a full list of arguments, refer to the [Arguments](#arguments) section.
docs/en/modes/export.md 0 → 100644
View file @ af155c51
---
comments: true
description: Learn how to export your YOLO11 model to various formats like ONNX, TensorRT, and CoreML. Achieve maximum compatibility and performance.
keywords: YOLO11, Model Export, ONNX, TensorRT, CoreML, Ultralytics, AI, Machine Learning, Inference, Deployment
---
# Model Export with Ultralytics YOLO
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif" alt="Ultralytics YOLO ecosystem and integrations">
## Introduction
The ultimate goal of training a model is to deploy it for real-world applications. Export mode in Ultralytics YOLO11 offers a versatile range of options for exporting your trained model to different formats, making it deployable across various platforms and devices. This comprehensive guide aims to walk you through the nuances of model exporting, showcasing how to achieve maximum compatibility and performance.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/WbomGeoOT_k?si=aGmuyooWftA0ue9X"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How To Export Custom Trained Ultralytics YOLO Model and Run Live Inference on Webcam.
</p>
## Why Choose YOLO11's Export Mode?
- **Versatility:** Export to multiple formats including ONNX, TensorRT, CoreML, and more.
- **Performance:** Gain up to 5x GPU speedup with TensorRT and 3x CPU speedup with ONNX or OpenVINO.
- **Compatibility:** Make your model universally deployable across numerous hardware and software environments.
- **Ease of Use:** Simple CLI and Python API for quick and straightforward model exporting.
### Key Features of Export Mode
Here are some of the standout functionalities:
- **One-Click Export:** Simple commands for exporting to different formats.
- **Batch Export:** Export batched-inference capable models.
- **Optimized Inference:** Exported models are optimized for quicker inference times.
- **Tutorial Videos:** In-depth guides and tutorials for a smooth exporting experience.
!!! tip
* Export to [ONNX](../integrations/onnx.md) or [OpenVINO](../integrations/openvino.md) for up to 3x CPU speedup.
* Export to [TensorRT](../integrations/tensorrt.md) for up to 5x GPU speedup.
## Usage Examples
Export a YOLO11n model to a different format like ONNX or TensorRT. See the Arguments section below for a full list of export arguments.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained model
# Export the model
model.export(format="onnx")
```
=== "CLI"
```bash
yolo export model=yolo11n.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
## Arguments
This table details the configurations and options available for exporting YOLO models to different formats. These settings are critical for optimizing the exported model's performance, size, and compatibility across various platforms and environments. Proper configuration ensures that the model is ready for deployment in the intended application with optimal efficiency.
{% include "macros/export-args.md" %}
Adjusting these parameters allows for customization of the export process to fit specific requirements, such as deployment environment, hardware constraints, and performance targets. Selecting the appropriate format and settings is essential for achieving the best balance between model size, speed, and [accuracy](https://www.ultralytics.com/glossary/accuracy).
## Export Formats
Available YOLO11 export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolo11n.onnx`. Usage examples are shown for your model after export completes.
{% include "macros/export-table.md" %}
## FAQ
### How do I export a YOLO11 model to ONNX format?
Exporting a YOLO11 model to ONNX format is straightforward with Ultralytics. It provides both Python and CLI methods for exporting models.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained model
# Export the model
model.export(format="onnx")
```
=== "CLI"
```bash
yolo export model=yolo11n.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
For more details on the process, including advanced options like handling different input sizes, refer to the [ONNX section](../integrations/onnx.md).
### What are the benefits of using TensorRT for model export?
Using TensorRT for model export offers significant performance improvements. YOLO11 models exported to TensorRT can achieve up to a 5x GPU speedup, making it ideal for real-time inference applications.
- **Versatility:** Optimize models for a specific hardware setup.
- **Speed:** Achieve faster inference through advanced optimizations.
- **Compatibility:** Integrate smoothly with NVIDIA hardware.
To learn more about integrating TensorRT, see the [TensorRT integration guide](../integrations/tensorrt.md).
### How do I enable INT8 quantization when exporting my YOLO11 model?
INT8 quantization is an excellent way to compress the model and speed up inference, especially on edge devices. Here's how you can enable INT8 quantization:
!!! example
=== "Python"
```python
from ultralytics import YOLO
model = YOLO("yolo11n.pt") # Load a model
model.export(format="engine", int8=True)
```
=== "CLI"
```bash
yolo export model=yolo11n.pt format=engine int8=True # export TensorRT model with INT8 quantization
```
INT8 quantization can be applied to various formats, such as TensorRT and CoreML. More details can be found in the [Export section](../modes/export.md).
### Why is dynamic input size important when exporting models?
Dynamic input size allows the exported model to handle varying image dimensions, providing flexibility and optimizing processing efficiency for different use cases. When exporting to formats like ONNX or TensorRT, enabling dynamic input size ensures that the model can adapt to different input shapes seamlessly.
To enable this feature, use the `dynamic=True` flag during export:
!!! example
=== "Python"
```python
from ultralytics import YOLO
model = YOLO("yolo11n.pt")
model.export(format="onnx", dynamic=True)
```
=== "CLI"
```bash
yolo export model=yolo11n.pt format=onnx dynamic=True
```
For additional context, refer to the [dynamic input size configuration](#arguments).
### What are the key export arguments to consider for optimizing model performance?
Understanding and configuring export arguments is crucial for optimizing model performance:
- **`format:`** The target format for the exported model (e.g., `onnx`, `torchscript`, `tensorflow`).
- **`imgsz:`** Desired image size for the model input (e.g., `640` or `(height, width)`).
- **`half:`** Enables FP16 quantization, reducing model size and potentially speeding up inference.
- **`optimize:`** Applies specific optimizations for mobile or constrained environments.
- **`int8:`** Enables INT8 quantization, highly beneficial for edge deployments.
For a detailed list and explanations of all the export arguments, visit the [Export Arguments section](#arguments).
docs/en/modes/index.md 0 → 100644
View file @ af155c51
---
comments: true
description: Discover the diverse modes of Ultralytics YOLO11, including training, validation, prediction, export, tracking, and benchmarking. Maximize model performance and efficiency.
keywords: Ultralytics, YOLO11, machine learning, model training, validation, prediction, export, tracking, benchmarking, object detection
---
# Ultralytics YOLO11 Modes
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif" alt="Ultralytics YOLO ecosystem and integrations">
## Introduction
Ultralytics YOLO11 is not just another object detection model; it's a versatile framework designed to cover the entire lifecycle of [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models—from data ingestion and model training to validation, deployment, and real-world tracking. Each mode serves a specific purpose and is engineered to offer you the flexibility and efficiency required for different tasks and use-cases.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/j8uQc0qB91s?si=dhnGKgqvs7nPgeaM"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Ultralytics Modes Tutorial: Train, Validate, Predict, Export & Benchmark.
</p>
### Modes at a Glance
Understanding the different **modes** that Ultralytics YOLO11 supports is critical to getting the most out of your models:
- **Train** mode: Fine-tune your model on custom or preloaded datasets.
- **Val** mode: A post-training checkpoint to validate model performance.
- **Predict** mode: Unleash the predictive power of your model on real-world data.
- **Export** mode: Make your [model deployment](https://www.ultralytics.com/glossary/model-deployment)-ready in various formats.
- **Track** mode: Extend your object detection model into real-time tracking applications.
- **Benchmark** mode: Analyze the speed and accuracy of your model in diverse deployment environments.
This comprehensive guide aims to give you an overview and practical insights into each mode, helping you harness the full potential of YOLO11.
## [Train](train.md)
Train mode is used for training a YOLO11 model on a custom dataset. In this mode, the model is trained using the specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can accurately predict the classes and locations of objects in an image.
[Train Examples](train.md){ .md-button }
## [Val](val.md)
Val mode is used for validating a YOLO11 model after it has been trained. In this mode, the model is evaluated on a validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters of the model to improve its performance.
[Val Examples](val.md){ .md-button }
## [Predict](predict.md)
Predict mode is used for making predictions using a trained YOLO11 model on new images or videos. In this mode, the model is loaded from a checkpoint file, and the user can provide images or videos to perform inference. The model predicts the classes and locations of objects in the input images or videos.
[Predict Examples](predict.md){ .md-button }
## [Export](export.md)
Export mode is used for exporting a YOLO11 model to a format that can be used for deployment. In this mode, the model is converted to a format that can be used by other software applications or hardware devices. This mode is useful when deploying the model to production environments.
[Export Examples](export.md){ .md-button }
## [Track](track.md)
Track mode is used for tracking objects in real-time using a YOLO11 model. In this mode, the model is loaded from a checkpoint file, and the user can provide a live video stream to perform real-time object tracking. This mode is useful for applications such as surveillance systems or self-driving cars.
[Track Examples](track.md){ .md-button }
## [Benchmark](benchmark.md)
Benchmark mode is used to profile the speed and accuracy of various export formats for YOLO11. The benchmarks provide information on the size of the exported format, its `mAP50-95` metrics (for object detection, segmentation, and pose) or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various formats like ONNX, OpenVINO, TensorRT, and others. This information can help users choose the optimal export format for their specific use case based on their requirements for speed and accuracy.
[Benchmark Examples](benchmark.md){ .md-button }
## FAQ
### How do I train a custom [object detection](https://www.ultralytics.com/glossary/object-detection) model with Ultralytics YOLO11?
Training a custom object detection model with Ultralytics YOLO11 involves using the train mode. You need a dataset formatted in YOLO format, containing images and corresponding annotation files. Use the following command to start the training process:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a pre-trained YOLO model (you can choose n, s, m, l, or x versions)
model = YOLO("yolo11n.pt")
# Start training on your custom dataset
model.train(data="path/to/dataset.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Train a YOLO model from the command line
yolo train data=path/to/dataset.yaml epochs=100 imgsz=640
```
For more detailed instructions, you can refer to the [Ultralytics Train Guide](../modes/train.md).
### What metrics does Ultralytics YOLO11 use to validate the model's performance?
Ultralytics YOLO11 uses various metrics during the validation process to assess model performance. These include:
- **mAP (mean Average Precision)**: This evaluates the accuracy of object detection.
- **IOU (Intersection over Union)**: Measures the overlap between predicted and ground truth bounding boxes.
- **[Precision](https://www.ultralytics.com/glossary/precision) and [Recall](https://www.ultralytics.com/glossary/recall)**: Precision measures the ratio of true positive detections to the total detected positives, while recall measures the ratio of true positive detections to the total actual positives.
You can run the following command to start the validation:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a pre-trained or custom YOLO model
model = YOLO("yolo11n.pt")
# Run validation on your dataset
model.val(data="path/to/validation.yaml")
```
=== "CLI"
```bash
# Validate a YOLO model from the command line
yolo val data=path/to/validation.yaml
```
Refer to the [Validation Guide](../modes/val.md) for further details.
### How can I export my YOLO11 model for deployment?
Ultralytics YOLO11 offers export functionality to convert your trained model into various deployment formats such as ONNX, TensorRT, CoreML, and more. Use the following example to export your model:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load your trained YOLO model
model = YOLO("yolo11n.pt")
# Export the model to ONNX format (you can specify other formats as needed)
model.export(format="onnx")
```
=== "CLI"
```bash
# Export a YOLO model to ONNX format from the command line
yolo export model=yolo11n.pt format=onnx
```
Detailed steps for each export format can be found in the [Export Guide](../modes/export.md).
### What is the purpose of the benchmark mode in Ultralytics YOLO11?
Benchmark mode in Ultralytics YOLO11 is used to analyze the speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) of various export formats such as ONNX, TensorRT, and OpenVINO. It provides metrics like model size, `mAP50-95` for object detection, and inference time across different hardware setups, helping you choose the most suitable format for your deployment needs.
!!! example
=== "Python"
```python
from ultralytics.utils.benchmarks import benchmark
# Run benchmark on GPU (device 0)
# You can adjust parameters like model, dataset, image size, and precision as needed
benchmark(model="yolo11n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
```
=== "CLI"
```bash
# Benchmark a YOLO model from the command line
# Adjust parameters as needed for your specific use case
yolo benchmark model=yolo11n.pt data='coco8.yaml' imgsz=640 half=False device=0
```
For more details, refer to the [Benchmark Guide](../modes/benchmark.md).
### How can I perform real-time object tracking using Ultralytics YOLO11?
Real-time object tracking can be achieved using the track mode in Ultralytics YOLO11. This mode extends object detection capabilities to track objects across video frames or live feeds. Use the following example to enable tracking:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a pre-trained YOLO model
model = YOLO("yolo11n.pt")
# Start tracking objects in a video
# You can also use live video streams or webcam input
model.track(source="path/to/video.mp4")
```
=== "CLI"
```bash
# Perform object tracking on a video from the command line
# You can specify different sources like webcam (0) or RTSP streams
yolo track source=path/to/video.mp4
```
For in-depth instructions, visit the [Track Guide](../modes/track.md).
docs/en/modes/predict.md 0 → 100644
View file @ af155c51
---
comments: true
description: Harness the power of Ultralytics YOLO11 for real-time, high-speed inference on various data sources. Learn about predict mode, key features, and practical applications.
keywords: Ultralytics, YOLO11, model prediction, inference, predict mode, real-time inference, computer vision, machine learning, streaming, high performance
---
# Model Prediction with Ultralytics YOLO
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif" alt="Ultralytics YOLO ecosystem and integrations">
## Introduction
In the world of [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv), the process of making sense out of visual data is called 'inference' or 'prediction'. Ultralytics YOLO11 offers a powerful feature known as **predict mode** that is tailored for high-performance, real-time inference on a wide range of data sources.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/QtsI0TnwDZs?si=ljesw75cMO2Eas14"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How to Extract the Outputs from Ultralytics YOLO Model for Custom Projects.
</p>
## Real-world Applications
| Manufacturing | Sports | Safety |
| :-----------------------------------------------: | :--------------------------------------------------: | :-----------------------------------------: |
| ![Vehicle Spare Parts Detection][car spare parts] | ![Football Player Detection][football player detect] | ![People Fall Detection][human fall detect] |
| Vehicle Spare Parts Detection | Football Player Detection | People Fall Detection |
## Why Use Ultralytics YOLO for Inference?
Here's why you should consider YOLO11's predict mode for your various inference needs:
- **Versatility:** Capable of making inferences on images, videos, and even live streams.
- **Performance:** Engineered for real-time, high-speed processing without sacrificing [accuracy](https://www.ultralytics.com/glossary/accuracy).
- **Ease of Use:** Intuitive Python and CLI interfaces for rapid deployment and testing.
- **Highly Customizable:** Various settings and parameters to tune the model's inference behavior according to your specific requirements.
### Key Features of Predict Mode
YOLO11's predict mode is designed to be robust and versatile, featuring:
- **Multiple Data Source Compatibility:** Whether your data is in the form of individual images, a collection of images, video files, or real-time video streams, predict mode has you covered.
- **Streaming Mode:** Use the streaming feature to generate a memory-efficient generator of `Results` objects. Enable this by setting `stream=True` in the predictor's call method.
- **Batch Processing:** The ability to process multiple images or video frames in a single batch, further speeding up inference time.
- **Integration Friendly:** Easily integrate with existing data pipelines and other software components, thanks to its flexible API.
Ultralytics YOLO models return either a Python list of `Results` objects, or a memory-efficient Python generator of `Results` objects when `stream=True` is passed to the model during inference:
!!! example "Predict"
=== "Return a list with `stream=False`"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # pretrained YOLO11n model
# Run batched inference on a list of images
results = model(["image1.jpg", "image2.jpg"]) # return a list of Results objects
# Process results list
for result in results:
boxes = result.boxes # Boxes object for bounding box outputs
masks = result.masks # Masks object for segmentation masks outputs
keypoints = result.keypoints # Keypoints object for pose outputs
probs = result.probs # Probs object for classification outputs
obb = result.obb # Oriented boxes object for OBB outputs
result.show() # display to screen
result.save(filename="result.jpg") # save to disk
```
=== "Return a generator with `stream=True`"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # pretrained YOLO11n model
# Run batched inference on a list of images
results = model(["image1.jpg", "image2.jpg"], stream=True) # return a generator of Results objects
# Process results generator
for result in results:
boxes = result.boxes # Boxes object for bounding box outputs
masks = result.masks # Masks object for segmentation masks outputs
keypoints = result.keypoints # Keypoints object for pose outputs
probs = result.probs # Probs object for classification outputs
obb = result.obb # Oriented boxes object for OBB outputs
result.show() # display to screen
result.save(filename="result.jpg") # save to disk
```
## Inference Sources
YOLO11 can process different types of input sources for inference, as shown in the table below. The sources include static images, video streams, and various data formats. The table also indicates whether each source can be used in streaming mode with the argument `stream=True` ✅. Streaming mode is beneficial for processing videos or live streams as it creates a generator of results instead of loading all frames into memory.
!!! tip
Use `stream=True` for processing long videos or large datasets to efficiently manage memory. When `stream=False`, the results for all frames or data points are stored in memory, which can quickly add up and cause out-of-memory errors for large inputs. In contrast, `stream=True` utilizes a generator, which only keeps the results of the current frame or data point in memory, significantly reducing memory consumption and preventing out-of-memory issues.
| Source | Example | Type | Notes |
| ----------------------------------------------------- | ------------------------------------------ | --------------- | ------------------------------------------------------------------------------------------- |
| image | `'image.jpg'` | `str` or `Path` | Single image file. |
| URL | `'https://ultralytics.com/images/bus.jpg'` | `str` | URL to an image. |
| screenshot | `'screen'` | `str` | Capture a screenshot. |
| PIL | `Image.open('image.jpg')` | `PIL.Image` | HWC format with RGB channels. |
| [OpenCV](https://www.ultralytics.com/glossary/opencv) | `cv2.imread('image.jpg')` | `np.ndarray` | HWC format with BGR channels `uint8 (0-255)`. |
| numpy | `np.zeros((640,1280,3))` | `np.ndarray` | HWC format with BGR channels `uint8 (0-255)`. |
| torch | `torch.zeros(16,3,320,640)` | `torch.Tensor` | BCHW format with RGB channels `float32 (0.0-1.0)`. |
| CSV | `'sources.csv'` | `str` or `Path` | CSV file containing paths to images, videos, or directories. |
| video ✅ | `'video.mp4'` | `str` or `Path` | Video file in formats like MP4, AVI, etc. |
| directory ✅ | `'path/'` | `str` or `Path` | Path to a directory containing images or videos. |
| glob ✅ | `'path/*.jpg'` | `str` | Glob pattern to match multiple files. Use the `*` character as a wildcard. |
| YouTube ✅ | `'https://youtu.be/LNwODJXcvt4'` | `str` | URL to a YouTube video. |
| stream ✅ | `'rtsp://example.com/media.mp4'` | `str` | URL for streaming protocols such as RTSP, RTMP, TCP, or an IP address. |
| multi-stream ✅ | `'list.streams'` | `str` or `Path` | `*.streams` text file with one stream URL per row, i.e. 8 streams will run at batch-size 8. |
| webcam ✅ | `0` | `int` | Index of the connected camera device to run inference on. |
Below are code examples for using each source type:
!!! example "Prediction sources"
=== "image"
Run inference on an image file.
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Define path to the image file
source = "path/to/image.jpg"
# Run inference on the source
results = model(source) # list of Results objects
```
=== "screenshot"
Run inference on the current screen content as a screenshot.
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Define current screenshot as source
source = "screen"
# Run inference on the source
results = model(source) # list of Results objects
```
=== "URL"
Run inference on an image or video hosted remotely via URL.
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Define remote image or video URL
source = "https://ultralytics.com/images/bus.jpg"
# Run inference on the source
results = model(source) # list of Results objects
```
=== "PIL"
Run inference on an image opened with Python Imaging Library (PIL).
```python
from PIL import Image
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Open an image using PIL
source = Image.open("path/to/image.jpg")
# Run inference on the source
results = model(source) # list of Results objects
```
=== "OpenCV"
Run inference on an image read with OpenCV.
```python
import cv2
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Read an image using OpenCV
source = cv2.imread("path/to/image.jpg")
# Run inference on the source
results = model(source) # list of Results objects
```
=== "numpy"
Run inference on an image represented as a numpy array.
```python
import numpy as np
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Create a random numpy array of HWC shape (640, 640, 3) with values in range [0, 255] and type uint8
source = np.random.randint(low=0, high=255, size=(640, 640, 3), dtype="uint8")
# Run inference on the source
results = model(source) # list of Results objects
```
=== "torch"
Run inference on an image represented as a [PyTorch](https://www.ultralytics.com/glossary/pytorch) tensor.
```python
import torch
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Create a random torch tensor of BCHW shape (1, 3, 640, 640) with values in range [0, 1] and type float32
source = torch.rand(1, 3, 640, 640, dtype=torch.float32)
# Run inference on the source
results = model(source) # list of Results objects
```
=== "CSV"
Run inference on a collection of images, URLs, videos and directories listed in a CSV file.
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Define a path to a CSV file with images, URLs, videos and directories
source = "path/to/file.csv"
# Run inference on the source
results = model(source) # list of Results objects
```
=== "video"
Run inference on a video file. By using `stream=True`, you can create a generator of Results objects to reduce memory usage.
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Define path to video file
source = "path/to/video.mp4"
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
```
=== "directory"
Run inference on all images and videos in a directory. To also capture images and videos in subdirectories use a glob pattern, i.e. `path/to/dir/**/*`.
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Define path to directory containing images and videos for inference
source = "path/to/dir"
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
```
=== "glob"
Run inference on all images and videos that match a glob expression with `*` characters.
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Define a glob search for all JPG files in a directory
source = "path/to/dir/*.jpg"
# OR define a recursive glob search for all JPG files including subdirectories
source = "path/to/dir/**/*.jpg"
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
```
=== "YouTube"
Run inference on a YouTube video. By using `stream=True`, you can create a generator of Results objects to reduce memory usage for long videos.
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Define source as YouTube video URL
source = "https://youtu.be/LNwODJXcvt4"
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
```
=== "Stream"
Use the stream mode to run inference on live video streams using RTSP, RTMP, TCP, or IP address protocols. If a single stream is provided, the model runs inference with a [batch size](https://www.ultralytics.com/glossary/batch-size) of 1. For multiple streams, a `.streams` text file can be used to perform batched inference, where the batch size is determined by the number of streams provided (e.g., batch-size 8 for 8 streams).
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Single stream with batch-size 1 inference
source = "rtsp://example.com/media.mp4" # RTSP, RTMP, TCP, or IP streaming address
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
```
For single stream usage, the batch size is set to 1 by default, allowing efficient real-time processing of the video feed.
=== "Multi-Stream"
To handle multiple video streams simultaneously, use a `.streams` text file containing the streaming sources. The model will run batched inference where the batch size equals the number of streams. This setup enables efficient processing of multiple feeds concurrently.
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Multiple streams with batched inference (e.g., batch-size 8 for 8 streams)
source = "path/to/list.streams" # *.streams text file with one streaming address per line
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
```
Example `.streams` text file:
```txt
rtsp://example.com/media1.mp4
rtsp://example.com/media2.mp4
rtmp://example2.com/live
tcp://192.168.1.100:554
...
```
Each row in the file represents a streaming source, allowing you to monitor and perform inference on several video streams at once.
=== "Webcam"
You can run inference on a connected camera device by passing the index of that particular camera to `source`.
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Run inference on the source
results = model(source=0, stream=True) # generator of Results objects
```
## Inference Arguments
`model.predict()` accepts multiple arguments that can be passed at inference time to override defaults:
!!! example
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Run inference on 'bus.jpg' with arguments
model.predict("bus.jpg", save=True, imgsz=320, conf=0.5)
```
Inference arguments:
{% include "macros/predict-args.md" %}
Visualization arguments:
{% include "macros/visualization-args.md" %}
## Image and Video Formats
YOLO11 supports various image and video formats, as specified in [ultralytics/data/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/utils.py). See the tables below for the valid suffixes and example predict commands.
### Images
The below table contains valid Ultralytics image formats.
!!! note
HEIC images are supported for inference only, not for training.
| Image Suffixes | Example Predict Command | Reference |
| -------------- | -------------------------------- | -------------------------------------------------------------------------- |
| `.bmp` | `yolo predict source=image.bmp` | [Microsoft BMP File Format](https://en.wikipedia.org/wiki/BMP_file_format) |
| `.dng` | `yolo predict source=image.dng` | [Adobe DNG](https://en.wikipedia.org/wiki/Digital_Negative) |
| `.jpeg` | `yolo predict source=image.jpeg` | [JPEG](https://en.wikipedia.org/wiki/JPEG) |
| `.jpg` | `yolo predict source=image.jpg` | [JPEG](https://en.wikipedia.org/wiki/JPEG) |
| `.mpo` | `yolo predict source=image.mpo` | [Multi Picture Object](https://fileinfo.com/extension/mpo) |
| `.png` | `yolo predict source=image.png` | [Portable Network Graphics](https://en.wikipedia.org/wiki/PNG) |
| `.tif` | `yolo predict source=image.tif` | [Tag Image File Format](https://en.wikipedia.org/wiki/TIFF) |
| `.tiff` | `yolo predict source=image.tiff` | [Tag Image File Format](https://en.wikipedia.org/wiki/TIFF) |
| `.webp` | `yolo predict source=image.webp` | [WebP](https://en.wikipedia.org/wiki/WebP) |
| `.pfm` | `yolo predict source=image.pfm` | [Portable FloatMap](https://en.wikipedia.org/wiki/Netpbm#File_formats) |
| `.HEIC` | `yolo predict source=image.HEIC` | [High Efficiency Image Format](https://en.wikipedia.org/wiki/HEIF) |
### Videos
The below table contains valid Ultralytics video formats.
| Video Suffixes | Example Predict Command | Reference |
| -------------- | -------------------------------- | -------------------------------------------------------------------------------- |
| `.asf` | `yolo predict source=video.asf` | [Advanced Systems Format](https://en.wikipedia.org/wiki/Advanced_Systems_Format) |
| `.avi` | `yolo predict source=video.avi` | [Audio Video Interleave](https://en.wikipedia.org/wiki/Audio_Video_Interleave) |
| `.gif` | `yolo predict source=video.gif` | [Graphics Interchange Format](https://en.wikipedia.org/wiki/GIF) |
| `.m4v` | `yolo predict source=video.m4v` | [MPEG-4 Part 14](https://en.wikipedia.org/wiki/M4V) |
| `.mkv` | `yolo predict source=video.mkv` | [Matroska](https://en.wikipedia.org/wiki/Matroska) |
| `.mov` | `yolo predict source=video.mov` | [QuickTime File Format](https://en.wikipedia.org/wiki/QuickTime_File_Format) |
| `.mp4` | `yolo predict source=video.mp4` | [MPEG-4 Part 14 - Wikipedia](https://en.wikipedia.org/wiki/MPEG-4_Part_14) |
| `.mpeg` | `yolo predict source=video.mpeg` | [MPEG-1 Part 2](https://en.wikipedia.org/wiki/MPEG-1) |
| `.mpg` | `yolo predict source=video.mpg` | [MPEG-1 Part 2](https://en.wikipedia.org/wiki/MPEG-1) |
| `.ts` | `yolo predict source=video.ts` | [MPEG Transport Stream](https://en.wikipedia.org/wiki/MPEG_transport_stream) |
| `.wmv` | `yolo predict source=video.wmv` | [Windows Media Video](https://en.wikipedia.org/wiki/Windows_Media_Video) |
| `.webm` | `yolo predict source=video.webm` | [WebM Project](https://en.wikipedia.org/wiki/WebM) |
## Working with Results
All Ultralytics `predict()` calls will return a list of `Results` objects:
!!! example "Results"
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Run inference on an image
results = model("bus.jpg") # list of 1 Results object
results = model(["bus.jpg", "zidane.jpg"]) # list of 2 Results objects
```
`Results` objects have the following attributes:
| Attribute | Type | Description |
| ------------ | --------------------- | ---------------------------------------------------------------------------------------- |
| `orig_img` | `numpy.ndarray` | The original image as a numpy array. |
| `orig_shape` | `tuple` | The original image shape in (height, width) format. |
| `boxes` | `Boxes, optional` | A Boxes object containing the detection bounding boxes. |
| `masks` | `Masks, optional` | A Masks object containing the detection masks. |
| `probs` | `Probs, optional` | A Probs object containing probabilities of each class for classification task. |
| `keypoints` | `Keypoints, optional` | A Keypoints object containing detected keypoints for each object. |
| `obb` | `OBB, optional` | An OBB object containing oriented bounding boxes. |
| `speed` | `dict` | A dictionary of preprocess, inference, and postprocess speeds in milliseconds per image. |
| `names` | `dict` | A dictionary of class names. |
| `path` | `str` | The path to the image file. |
`Results` objects have the following methods:
| Method | Return Type | Description |
| ------------- | --------------- | ----------------------------------------------------------------------------------- |
| `update()` | `None` | Update the boxes, masks, and probs attributes of the Results object. |
| `cpu()` | `Results` | Return a copy of the Results object with all tensors on CPU memory. |
| `numpy()` | `Results` | Return a copy of the Results object with all tensors as numpy arrays. |
| `cuda()` | `Results` | Return a copy of the Results object with all tensors on GPU memory. |
| `to()` | `Results` | Return a copy of the Results object with tensors on the specified device and dtype. |
| `new()` | `Results` | Return a new Results object with the same image, path, and names. |
| `plot()` | `numpy.ndarray` | Plots the detection results. Returns a numpy array of the annotated image. |
| `show()` | `None` | Show annotated results to screen. |
| `save()` | `None` | Save annotated results to file. |
| `verbose()` | `str` | Return log string for each task. |
| `save_txt()` | `None` | Save predictions into a txt file. |
| `save_crop()` | `None` | Save cropped predictions to `save_dir/cls/file_name.jpg`. |
| `tojson()` | `str` | Convert the object to JSON format. |
For more details see the [`Results` class documentation](../reference/engine/results.md).
### Boxes
`Boxes` object can be used to index, manipulate, and convert bounding boxes to different formats.
!!! example "Boxes"
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Run inference on an image
results = model("bus.jpg") # results list
# View results
for r in results:
print(r.boxes) # print the Boxes object containing the detection bounding boxes
```
Here is a table for the `Boxes` class methods and properties, including their name, type, and description:
| Name | Type | Description |
| --------- | ------------------------- | ------------------------------------------------------------------ |
| `cpu()` | Method | Move the object to CPU memory. |
| `numpy()` | Method | Convert the object to a numpy array. |
| `cuda()` | Method | Move the object to CUDA memory. |
| `to()` | Method | Move the object to the specified device. |
| `xyxy` | Property (`torch.Tensor`) | Return the boxes in xyxy format. |
| `conf` | Property (`torch.Tensor`) | Return the confidence values of the boxes. |
| `cls` | Property (`torch.Tensor`) | Return the class values of the boxes. |
| `id` | Property (`torch.Tensor`) | Return the track IDs of the boxes (if available). |
| `xywh` | Property (`torch.Tensor`) | Return the boxes in xywh format. |
| `xyxyn` | Property (`torch.Tensor`) | Return the boxes in xyxy format normalized by original image size. |
| `xywhn` | Property (`torch.Tensor`) | Return the boxes in xywh format normalized by original image size. |
For more details see the [`Boxes` class documentation](../reference/engine/results.md#ultralytics.engine.results.Boxes).
### Masks
`Masks` object can be used index, manipulate and convert masks to segments.
!!! example "Masks"
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n-seg Segment model
model = YOLO("yolo11n-seg.pt")
# Run inference on an image
results = model("bus.jpg") # results list
# View results
for r in results:
print(r.masks) # print the Masks object containing the detected instance masks
```
Here is a table for the `Masks` class methods and properties, including their name, type, and description:
| Name | Type | Description |
| --------- | ------------------------- | --------------------------------------------------------------- |
| `cpu()` | Method | Returns the masks tensor on CPU memory. |
| `numpy()` | Method | Returns the masks tensor as a numpy array. |
| `cuda()` | Method | Returns the masks tensor on GPU memory. |
| `to()` | Method | Returns the masks tensor with the specified device and dtype. |
| `xyn` | Property (`torch.Tensor`) | A list of normalized segments represented as tensors. |
| `xy` | Property (`torch.Tensor`) | A list of segments in pixel coordinates represented as tensors. |
For more details see the [`Masks` class documentation](../reference/engine/results.md#ultralytics.engine.results.Masks).
### Keypoints
`Keypoints` object can be used index, manipulate and normalize coordinates.
!!! example "Keypoints"
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n-pose Pose model
model = YOLO("yolo11n-pose.pt")
# Run inference on an image
results = model("bus.jpg") # results list
# View results
for r in results:
print(r.keypoints) # print the Keypoints object containing the detected keypoints
```
Here is a table for the `Keypoints` class methods and properties, including their name, type, and description:
| Name | Type | Description |
| --------- | ------------------------- | ----------------------------------------------------------------- |
| `cpu()` | Method | Returns the keypoints tensor on CPU memory. |
| `numpy()` | Method | Returns the keypoints tensor as a numpy array. |
| `cuda()` | Method | Returns the keypoints tensor on GPU memory. |
| `to()` | Method | Returns the keypoints tensor with the specified device and dtype. |
| `xyn` | Property (`torch.Tensor`) | A list of normalized keypoints represented as tensors. |
| `xy` | Property (`torch.Tensor`) | A list of keypoints in pixel coordinates represented as tensors. |
| `conf` | Property (`torch.Tensor`) | Returns confidence values of keypoints if available, else None. |
For more details see the [`Keypoints` class documentation](../reference/engine/results.md#ultralytics.engine.results.Keypoints).
### Probs
`Probs` object can be used index, get `top1` and `top5` indices and scores of classification.
!!! example "Probs"
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n-cls Classify model
model = YOLO("yolo11n-cls.pt")
# Run inference on an image
results = model("bus.jpg") # results list
# View results
for r in results:
print(r.probs) # print the Probs object containing the detected class probabilities
```
Here's a table summarizing the methods and properties for the `Probs` class:
| Name | Type | Description |
| ---------- | ------------------------- | ----------------------------------------------------------------------- |
| `cpu()` | Method | Returns a copy of the probs tensor on CPU memory. |
| `numpy()` | Method | Returns a copy of the probs tensor as a numpy array. |
| `cuda()` | Method | Returns a copy of the probs tensor on GPU memory. |
| `to()` | Method | Returns a copy of the probs tensor with the specified device and dtype. |
| `top1` | Property (`int`) | Index of the top 1 class. |
| `top5` | Property (`list[int]`) | Indices of the top 5 classes. |
| `top1conf` | Property (`torch.Tensor`) | Confidence of the top 1 class. |
| `top5conf` | Property (`torch.Tensor`) | Confidences of the top 5 classes. |
For more details see the [`Probs` class documentation](../reference/engine/results.md#ultralytics.engine.results.Probs).
### OBB
`OBB` object can be used to index, manipulate, and convert oriented bounding boxes to different formats.
!!! example "OBB"
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n-obb.pt")
# Run inference on an image
results = model("boats.jpg") # results list
# View results
for r in results:
print(r.obb) # print the OBB object containing the oriented detection bounding boxes
```
Here is a table for the `OBB` class methods and properties, including their name, type, and description:
| Name | Type | Description |
| ----------- | ------------------------- | --------------------------------------------------------------------- |
| `cpu()` | Method | Move the object to CPU memory. |
| `numpy()` | Method | Convert the object to a numpy array. |
| `cuda()` | Method | Move the object to CUDA memory. |
| `to()` | Method | Move the object to the specified device. |
| `conf` | Property (`torch.Tensor`) | Return the confidence values of the boxes. |
| `cls` | Property (`torch.Tensor`) | Return the class values of the boxes. |
| `id` | Property (`torch.Tensor`) | Return the track IDs of the boxes (if available). |
| `xyxy` | Property (`torch.Tensor`) | Return the horizontal boxes in xyxy format. |
| `xywhr` | Property (`torch.Tensor`) | Return the rotated boxes in xywhr format. |
| `xyxyxyxy` | Property (`torch.Tensor`) | Return the rotated boxes in xyxyxyxy format. |
| `xyxyxyxyn` | Property (`torch.Tensor`) | Return the rotated boxes in xyxyxyxy format normalized by image size. |
For more details see the [`OBB` class documentation](../reference/engine/results.md#ultralytics.engine.results.OBB).
## Plotting Results
The `plot()` method in `Results` objects facilitates visualization of predictions by overlaying detected objects (such as bounding boxes, masks, keypoints, and probabilities) onto the original image. This method returns the annotated image as a NumPy array, allowing for easy display or saving.
!!! example "Plotting"
```python
from PIL import Image
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Run inference on 'bus.jpg'
results = model(["bus.jpg", "zidane.jpg"]) # results list
# Visualize the results
for i, r in enumerate(results):
# Plot results image
im_bgr = r.plot() # BGR-order numpy array
im_rgb = Image.fromarray(im_bgr[..., ::-1]) # RGB-order PIL image
# Show results to screen (in supported environments)
r.show()
# Save results to disk
r.save(filename=f"results{i}.jpg")
```
### `plot()` Method Parameters
The `plot()` method supports various arguments to customize the output:
| Argument | Type | Description | Default |
| ------------ | --------------- | -------------------------------------------------------------------------- | ------------- |
| `conf` | `bool` | Include detection confidence scores. | `True` |
| `line_width` | `float` | Line width of bounding boxes. Scales with image size if `None`. | `None` |
| `font_size` | `float` | Text font size. Scales with image size if `None`. | `None` |
| `font` | `str` | Font name for text annotations. | `'Arial.ttf'` |
| `pil` | `bool` | Return image as a PIL Image object. | `False` |
| `img` | `numpy.ndarray` | Alternative image for plotting. Uses the original image if `None`. | `None` |
| `im_gpu` | `torch.Tensor` | GPU-accelerated image for faster mask plotting. Shape: (1, 3, 640, 640). | `None` |
| `kpt_radius` | `int` | Radius for drawn keypoints. | `5` |
| `kpt_line` | `bool` | Connect keypoints with lines. | `True` |
| `labels` | `bool` | Include class labels in annotations. | `True` |
| `boxes` | `bool` | Overlay bounding boxes on the image. | `True` |
| `masks` | `bool` | Overlay masks on the image. | `True` |
| `probs` | `bool` | Include classification probabilities. | `True` |
| `show` | `bool` | Display the annotated image directly using the default image viewer. | `False` |
| `save` | `bool` | Save the annotated image to a file specified by `filename`. | `False` |
| `filename` | `str` | Path and name of the file to save the annotated image if `save` is `True`. | `None` |
| `color_mode` | `str` | Specify the color mode, e.g., 'instance' or 'class'. | `'class'` |
## Thread-Safe Inference
Ensuring thread safety during inference is crucial when you are running multiple YOLO models in parallel across different threads. Thread-safe inference guarantees that each thread's predictions are isolated and do not interfere with one another, avoiding race conditions and ensuring consistent and reliable outputs.
When using YOLO models in a multi-threaded application, it's important to instantiate separate model objects for each thread or employ thread-local storage to prevent conflicts:
!!! example "Thread-Safe Inference"
Instantiate a single model inside each thread for thread-safe inference:
```python
from threading import Thread
from ultralytics import YOLO
def thread_safe_predict(model, image_path):
"""Performs thread-safe prediction on an image using a locally instantiated YOLO model."""
model = YOLO(model)
results = model.predict(image_path)
# Process results
# Starting threads that each have their own model instance
Thread(target=thread_safe_predict, args=("yolo11n.pt", "image1.jpg")).start()
Thread(target=thread_safe_predict, args=("yolo11n.pt", "image2.jpg")).start()
```
For an in-depth look at thread-safe inference with YOLO models and step-by-step instructions, please refer to our [YOLO Thread-Safe Inference Guide](../guides/yolo-thread-safe-inference.md). This guide will provide you with all the necessary information to avoid common pitfalls and ensure that your multi-threaded inference runs smoothly.
## Streaming Source `for`-loop
Here's a Python script using OpenCV (`cv2`) and YOLO to run inference on video frames. This script assumes you have already installed the necessary packages (`opencv-python` and `ultralytics`).
!!! example "Streaming for-loop"
```python
import cv2
from ultralytics import YOLO
# Load the YOLO model
model = YOLO("yolo11n.pt")
# Open the video file
video_path = "path/to/your/video/file.mp4"
cap = cv2.VideoCapture(video_path)
# Loop through the video frames
while cap.isOpened():
# Read a frame from the video
success, frame = cap.read()
if success:
# Run YOLO inference on the frame
results = model(frame)
# Visualize the results on the frame
annotated_frame = results[0].plot()
# Display the annotated frame
cv2.imshow("YOLO Inference", annotated_frame)
# Break the loop if 'q' is pressed
if cv2.waitKey(1) & 0xFF == ord("q"):
break
else:
# Break the loop if the end of the video is reached
break
# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()
```
This script will run predictions on each frame of the video, visualize the results, and display them in a window. The loop can be exited by pressing 'q'.
[car spare parts]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/a0f802a8-0776-44cf-8f17-93974a4a28a1
[football player detect]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/7d320e1f-fc57-4d7f-a691-78ee579c3442
[human fall detect]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/86437c4a-3227-4eee-90ef-9efb697bdb43
## FAQ
### What is Ultralytics YOLO and its predict mode for real-time inference?
Ultralytics YOLO is a state-of-the-art model for real-time [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification. Its **predict mode** allows users to perform high-speed inference on various data sources such as images, videos, and live streams. Designed for performance and versatility, it also offers batch processing and streaming modes. For more details on its features, check out the [Ultralytics YOLO predict mode](#key-features-of-predict-mode).
### How can I run inference using Ultralytics YOLO on different data sources?
Ultralytics YOLO can process a wide range of data sources, including individual images, videos, directories, URLs, and streams. You can specify the data source in the `model.predict()` call. For example, use `'image.jpg'` for a local image or `'https://ultralytics.com/images/bus.jpg'` for a URL. Check out the detailed examples for various [inference sources](#inference-sources) in the documentation.
### How do I optimize YOLO inference speed and memory usage?
To optimize inference speed and manage memory efficiently, you can use the streaming mode by setting `stream=True` in the predictor's call method. The streaming mode generates a memory-efficient generator of `Results` objects instead of loading all frames into memory. For processing long videos or large datasets, streaming mode is particularly useful. Learn more about [streaming mode](#key-features-of-predict-mode).
### What inference arguments does Ultralytics YOLO support?
The `model.predict()` method in YOLO supports various arguments such as `conf`, `iou`, `imgsz`, `device`, and more. These arguments allow you to customize the inference process, setting parameters like confidence thresholds, image size, and the device used for computation. Detailed descriptions of these arguments can be found in the [inference arguments](#inference-arguments) section.
### How can I visualize and save the results of YOLO predictions?
After running inference with YOLO, the `Results` objects contain methods for displaying and saving annotated images. You can use methods like `result.show()` and `result.save(filename="result.jpg")` to visualize and save the results. For a comprehensive list of these methods, refer to the [working with results](#working-with-results) section.
docs/en/modes/track.md 0 → 100644
View file @ af155c51
---
comments: true
description: Discover efficient, flexible, and customizable multi-object tracking with Ultralytics YOLO. Learn to track real-time video streams with ease.
keywords: multi-object tracking, Ultralytics YOLO, video analytics, real-time tracking, object detection, AI, machine learning
---
# Multi-Object Tracking with Ultralytics YOLO
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/multi-object-tracking-examples.avif" alt="Multi-object tracking examples">
Object tracking in the realm of video analytics is a critical task that not only identifies the location and class of objects within the frame but also maintains a unique ID for each detected object as the video progresses. The applications are limitless—ranging from surveillance and security to real-time sports analytics.
## Why Choose Ultralytics YOLO for Object Tracking?
The output from Ultralytics trackers is consistent with standard [object detection](https://www.ultralytics.com/glossary/object-detection) but has the added value of object IDs. This makes it easy to track objects in video streams and perform subsequent analytics. Here's why you should consider using Ultralytics YOLO for your object tracking needs:
- **Efficiency:** Process video streams in real-time without compromising [accuracy](https://www.ultralytics.com/glossary/accuracy).
- **Flexibility:** Supports multiple tracking algorithms and configurations.
- **Ease of Use:** Simple Python API and CLI options for quick integration and deployment.
- **Customizability:** Easy to use with custom trained YOLO models, allowing integration into domain-specific applications.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/hHyHmOtmEgs?si=VNZtXmm45Nb9s-N-"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Object Detection and Tracking with Ultralytics YOLO.
</p>
## Real-world Applications
| Transportation | Retail | Aquaculture |
| :--------------------------------: | :------------------------------: | :--------------------------: |
| ![Vehicle Tracking][vehicle track] | ![People Tracking][people track] | ![Fish Tracking][fish track] |
| Vehicle Tracking | People Tracking | Fish Tracking |
## Features at a Glance
Ultralytics YOLO extends its object detection features to provide robust and versatile object tracking:
- **Real-Time Tracking:** Seamlessly track objects in high-frame-rate videos.
- **Multiple Tracker Support:** Choose from a variety of established tracking algorithms.
- **Customizable Tracker Configurations:** Tailor the tracking algorithm to meet specific requirements by adjusting various parameters.
## Available Trackers
Ultralytics YOLO supports the following tracking algorithms. They can be enabled by passing the relevant YAML configuration file such as `tracker=tracker_type.yaml`:
- [BoT-SORT](https://github.com/NirAharon/BoT-SORT) - Use `botsort.yaml` to enable this tracker.
- [ByteTrack](https://github.com/ifzhang/ByteTrack) - Use `bytetrack.yaml` to enable this tracker.
The default tracker is BoT-SORT.
## Tracking
!!! warning "Tracker Threshold Information"
If object confidence score will be low, i.e lower than [`track_high_thresh`](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/trackers/bytetrack.yaml#L5), then there will be no tracks successfully returned and updated.
To run the tracker on video streams, use a trained Detect, Segment or Pose model such as YOLO11n, YOLO11n-seg and YOLO11n-pose.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load an official or custom model
model = YOLO("yolo11n.pt") # Load an official Detect model
model = YOLO("yolo11n-seg.pt") # Load an official Segment model
model = YOLO("yolo11n-pose.pt") # Load an official Pose model
model = YOLO("path/to/best.pt") # Load a custom trained model
# Perform tracking with the model
results = model.track("https://youtu.be/LNwODJXcvt4", show=True) # Tracking with default tracker
results = model.track("https://youtu.be/LNwODJXcvt4", show=True, tracker="bytetrack.yaml") # with ByteTrack
```
=== "CLI"
```bash
# Perform tracking with various models using the command line interface
yolo track model=yolo11n.pt source="https://youtu.be/LNwODJXcvt4" # Official Detect model
yolo track model=yolo11n-seg.pt source="https://youtu.be/LNwODJXcvt4" # Official Segment model
yolo track model=yolo11n-pose.pt source="https://youtu.be/LNwODJXcvt4" # Official Pose model
yolo track model=path/to/best.pt source="https://youtu.be/LNwODJXcvt4" # Custom trained model
# Track using ByteTrack tracker
yolo track model=path/to/best.pt tracker="bytetrack.yaml"
```
As can be seen in the above usage, tracking is available for all Detect, Segment and Pose models run on videos or streaming sources.
## Configuration
!!! warning "Tracker Threshold Information"
If object confidence score will be low, i.e lower than [`track_high_thresh`](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/trackers/bytetrack.yaml#L5), then there will be no tracks successfully returned and updated.
### Tracking Arguments
Tracking configuration shares properties with Predict mode, such as `conf`, `iou`, and `show`. For further configurations, refer to the [Predict](../modes/predict.md#inference-arguments) model page.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Configure the tracking parameters and run the tracker
model = YOLO("yolo11n.pt")
results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True)
```
=== "CLI"
```bash
# Configure tracking parameters and run the tracker using the command line interface
yolo track model=yolo11n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3, iou=0.5 show
```
### Tracker Selection
Ultralytics also allows you to use a modified tracker configuration file. To do this, simply make a copy of a tracker config file (for example, `custom_tracker.yaml`) from [ultralytics/cfg/trackers](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers) and modify any configurations (except the `tracker_type`) as per your needs.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load the model and run the tracker with a custom configuration file
model = YOLO("yolo11n.pt")
results = model.track(source="https://youtu.be/LNwODJXcvt4", tracker="custom_tracker.yaml")
```
=== "CLI"
```bash
# Load the model and run the tracker with a custom configuration file using the command line interface
yolo track model=yolo11n.pt source="https://youtu.be/LNwODJXcvt4" tracker='custom_tracker.yaml'
```
For a comprehensive list of tracking arguments, refer to the [ultralytics/cfg/trackers](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers) page.
## Python Examples
### Persisting Tracks Loop
Here is a Python script using [OpenCV](https://www.ultralytics.com/glossary/opencv) (`cv2`) and YOLO11 to run object tracking on video frames. This script still assumes you have already installed the necessary packages (`opencv-python` and `ultralytics`). The `persist=True` argument tells the tracker that the current image or frame is the next in a sequence and to expect tracks from the previous image in the current image.
!!! example "Streaming for-loop with tracking"
```python
import cv2
from ultralytics import YOLO
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Open the video file
video_path = "path/to/video.mp4"
cap = cv2.VideoCapture(video_path)
# Loop through the video frames
while cap.isOpened():
# Read a frame from the video
success, frame = cap.read()
if success:
# Run YOLO11 tracking on the frame, persisting tracks between frames
results = model.track(frame, persist=True)
# Visualize the results on the frame
annotated_frame = results[0].plot()
# Display the annotated frame
cv2.imshow("YOLO11 Tracking", annotated_frame)
# Break the loop if 'q' is pressed
if cv2.waitKey(1) & 0xFF == ord("q"):
break
else:
# Break the loop if the end of the video is reached
break
# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()
```
Please note the change from `model(frame)` to `model.track(frame)`, which enables object tracking instead of simple detection. This modified script will run the tracker on each frame of the video, visualize the results, and display them in a window. The loop can be exited by pressing 'q'.
### Plotting Tracks Over Time
Visualizing object tracks over consecutive frames can provide valuable insights into the movement patterns and behavior of detected objects within a video. With Ultralytics YOLO11, plotting these tracks is a seamless and efficient process.
In the following example, we demonstrate how to utilize YOLO11's tracking capabilities to plot the movement of detected objects across multiple video frames. This script involves opening a video file, reading it frame by frame, and utilizing the YOLO model to identify and track various objects. By retaining the center points of the detected bounding boxes and connecting them, we can draw lines that represent the paths followed by the tracked objects.
!!! example "Plotting tracks over multiple video frames"
```python
from collections import defaultdict
import cv2
import numpy as np
from ultralytics import YOLO
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Open the video file
video_path = "path/to/video.mp4"
cap = cv2.VideoCapture(video_path)
# Store the track history
track_history = defaultdict(lambda: [])
# Loop through the video frames
while cap.isOpened():
# Read a frame from the video
success, frame = cap.read()
if success:
# Run YOLO11 tracking on the frame, persisting tracks between frames
results = model.track(frame, persist=True)
# Get the boxes and track IDs
boxes = results[0].boxes.xywh.cpu()
track_ids = results[0].boxes.id.int().cpu().tolist()
# Visualize the results on the frame
annotated_frame = results[0].plot()
# Plot the tracks
for box, track_id in zip(boxes, track_ids):
x, y, w, h = box
track = track_history[track_id]
track.append((float(x), float(y))) # x, y center point
if len(track) > 30: # retain 90 tracks for 90 frames
track.pop(0)
# Draw the tracking lines
points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
cv2.polylines(annotated_frame, [points], isClosed=False, color=(230, 230, 230), thickness=10)
# Display the annotated frame
cv2.imshow("YOLO11 Tracking", annotated_frame)
# Break the loop if 'q' is pressed
if cv2.waitKey(1) & 0xFF == ord("q"):
break
else:
# Break the loop if the end of the video is reached
break
# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()
```
### Multithreaded Tracking
Multithreaded tracking provides the capability to run object tracking on multiple video streams simultaneously. This is particularly useful when handling multiple video inputs, such as from multiple surveillance cameras, where concurrent processing can greatly enhance efficiency and performance.
In the provided Python script, we make use of Python's `threading` module to run multiple instances of the tracker concurrently. Each thread is responsible for running the tracker on one video file, and all the threads run simultaneously in the background.
To ensure that each thread receives the correct parameters (the video file, the model to use and the file index), we define a function `run_tracker_in_thread` that accepts these parameters and contains the main tracking loop. This function reads the video frame by frame, runs the tracker, and displays the results.
Two different models are used in this example: `yolo11n.pt` and `yolo11n-seg.pt`, each tracking objects in a different video file. The video files are specified in `video_file1` and `video_file2`.
The `daemon=True` parameter in `threading.Thread` means that these threads will be closed as soon as the main program finishes. We then start the threads with `start()` and use `join()` to make the main thread wait until both tracker threads have finished.
Finally, after all threads have completed their task, the windows displaying the results are closed using `cv2.destroyAllWindows()`.
!!! example "Streaming for-loop with tracking"
```python
import threading
import cv2
from ultralytics import YOLO
# Define model names and video sources
MODEL_NAMES = ["yolo11n.pt", "yolo11n-seg.pt"]
SOURCES = ["path/to/video.mp4", "0"] # local video, 0 for webcam
def run_tracker_in_thread(model_name, filename):
"""
Run YOLO tracker in its own thread for concurrent processing.
Args:
model_name (str): The YOLO11 model object.
filename (str): The path to the video file or the identifier for the webcam/external camera source.
"""
model = YOLO(model_name)
results = model.track(filename, save=True, stream=True)
for r in results:
pass
# Create and start tracker threads using a for loop
tracker_threads = []
for video_file, model_name in zip(SOURCES, MODEL_NAMES):
thread = threading.Thread(target=run_tracker_in_thread, args=(model_name, video_file), daemon=True)
tracker_threads.append(thread)
thread.start()
# Wait for all tracker threads to finish
for thread in tracker_threads:
thread.join()
# Clean up and close windows
cv2.destroyAllWindows()
```
This example can easily be extended to handle more video files and models by creating more threads and applying the same methodology.
## Contribute New Trackers
Are you proficient in multi-object tracking and have successfully implemented or adapted a tracking algorithm with Ultralytics YOLO? We invite you to contribute to our Trackers section in [ultralytics/cfg/trackers](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers)! Your real-world applications and solutions could be invaluable for users working on tracking tasks.
By contributing to this section, you help expand the scope of tracking solutions available within the Ultralytics YOLO framework, adding another layer of functionality and utility for the community.
To initiate your contribution, please refer to our [Contributing Guide](../help/contributing.md) for comprehensive instructions on submitting a Pull Request (PR) 🛠️. We are excited to see what you bring to the table!
Together, let's enhance the tracking capabilities of the Ultralytics YOLO ecosystem 🙏!
[fish track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/a5146d0f-bfa8-4e0a-b7df-3c1446cd8142
[people track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/93bb4ee2-77a0-4e4e-8eb6-eb8f527f0527
[vehicle track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/ee6e6038-383b-4f21-ac29-b2a1c7d386ab
## FAQ
### What is Multi-Object Tracking and how does Ultralytics YOLO support it?
Multi-object tracking in video analytics involves both identifying objects and maintaining a unique ID for each detected object across video frames. Ultralytics YOLO supports this by providing real-time tracking along with object IDs, facilitating tasks such as security surveillance and sports analytics. The system uses trackers like BoT-SORT and ByteTrack, which can be configured via YAML files.
### How do I configure a custom tracker for Ultralytics YOLO?
You can configure a custom tracker by copying an existing tracker configuration file (e.g., `custom_tracker.yaml`) from the [Ultralytics tracker configuration directory](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers) and modifying parameters as needed, except for the `tracker_type`. Use this file in your tracking model like so:
!!! example
=== "Python"
```python
from ultralytics import YOLO
model = YOLO("yolo11n.pt")
results = model.track(source="https://youtu.be/LNwODJXcvt4", tracker="custom_tracker.yaml")
```
=== "CLI"
```bash
yolo track model=yolo11n.pt source="https://youtu.be/LNwODJXcvt4" tracker='custom_tracker.yaml'
```
### How can I run object tracking on multiple video streams simultaneously?
To run object tracking on multiple video streams simultaneously, you can use Python's `threading` module. Each thread will handle a separate video stream. Here's an example of how you can set this up:
!!! example "Multithreaded Tracking"
```python
import threading
import cv2
from ultralytics import YOLO
# Define model names and video sources
MODEL_NAMES = ["yolo11n.pt", "yolo11n-seg.pt"]
SOURCES = ["path/to/video.mp4", "0"] # local video, 0 for webcam
def run_tracker_in_thread(model_name, filename):
"""
Run YOLO tracker in its own thread for concurrent processing.
Args:
model_name (str): The YOLO11 model object.
filename (str): The path to the video file or the identifier for the webcam/external camera source.
"""
model = YOLO(model_name)
results = model.track(filename, save=True, stream=True)
for r in results:
pass
# Create and start tracker threads using a for loop
tracker_threads = []
for video_file, model_name in zip(SOURCES, MODEL_NAMES):
thread = threading.Thread(target=run_tracker_in_thread, args=(model_name, video_file), daemon=True)
tracker_threads.append(thread)
thread.start()
# Wait for all tracker threads to finish
for thread in tracker_threads:
thread.join()
# Clean up and close windows
cv2.destroyAllWindows()
```
### What are the real-world applications of multi-object tracking with Ultralytics YOLO?
Multi-object tracking with Ultralytics YOLO has numerous applications, including:
- **Transportation:** Vehicle tracking for traffic management and autonomous driving.
- **Retail:** People tracking for in-store analytics and security.
- **Aquaculture:** Fish tracking for monitoring aquatic environments.
These applications benefit from Ultralytics YOLO's ability to process high-frame-rate videos in real time.
### How can I visualize object tracks over multiple video frames with Ultralytics YOLO?
To visualize object tracks over multiple video frames, you can use the YOLO model's tracking features along with OpenCV to draw the paths of detected objects. Here's an example script that demonstrates this:
!!! example "Plotting tracks over multiple video frames"
```python
from collections import defaultdict
import cv2
import numpy as np
from ultralytics import YOLO
model = YOLO("yolo11n.pt")
video_path = "path/to/video.mp4"
cap = cv2.VideoCapture(video_path)
track_history = defaultdict(lambda: [])
while cap.isOpened():
success, frame = cap.read()
if success:
results = model.track(frame, persist=True)
boxes = results[0].boxes.xywh.cpu()
track_ids = results[0].boxes.id.int().cpu().tolist()
annotated_frame = results[0].plot()
for box, track_id in zip(boxes, track_ids):
x, y, w, h = box
track = track_history[track_id]
track.append((float(x), float(y)))
if len(track) > 30:
track.pop(0)
points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
cv2.polylines(annotated_frame, [points], isClosed=False, color=(230, 230, 230), thickness=10)
cv2.imshow("YOLO11 Tracking", annotated_frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
else:
break
cap.release()
cv2.destroyAllWindows()
```
This script will plot the tracking lines showing the movement paths of the tracked objects over time.
docs/en/modes/train.md 0 → 100644
View file @ af155c51
---
comments: true
description: Learn how to efficiently train object detection models using YOLO11 with comprehensive instructions on settings, augmentation, and hardware utilization.
keywords: Ultralytics, YOLO11, model training, deep learning, object detection, GPU training, dataset augmentation, hyperparameter tuning, model performance, apple silicon training
---
# Model Training with Ultralytics YOLO
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif" alt="Ultralytics YOLO ecosystem and integrations">
## Introduction
Training a [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) model involves feeding it data and adjusting its parameters so that it can make accurate predictions. Train mode in Ultralytics YOLO11 is engineered for effective and efficient training of object detection models, fully utilizing modern hardware capabilities. This guide aims to cover all the details you need to get started with training your own models using YOLO11's robust set of features.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/LNwODJXcvt4?si=7n1UvGRLSd9p5wKs"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How to Train a YOLO model on Your Custom Dataset in Google Colab.
</p>
## Why Choose Ultralytics YOLO for Training?
Here are some compelling reasons to opt for YOLO11's Train mode:
- **Efficiency:** Make the most out of your hardware, whether you're on a single-GPU setup or scaling across multiple GPUs.
- **Versatility:** Train on custom datasets in addition to readily available ones like COCO, VOC, and ImageNet.
- **User-Friendly:** Simple yet powerful CLI and Python interfaces for a straightforward training experience.
- **Hyperparameter Flexibility:** A broad range of customizable hyperparameters to fine-tune model performance.
### Key Features of Train Mode
The following are some notable features of YOLO11's Train mode:
- **Automatic Dataset Download:** Standard datasets like COCO, VOC, and ImageNet are downloaded automatically on first use.
- **Multi-GPU Support:** Scale your training efforts seamlessly across multiple GPUs to expedite the process.
- **Hyperparameter Configuration:** The option to modify hyperparameters through YAML configuration files or CLI arguments.
- **Visualization and Monitoring:** Real-time tracking of training metrics and visualization of the learning process for better insights.
!!! tip
* YOLO11 datasets like COCO, VOC, ImageNet and many others automatically download on first use, i.e. `yolo train data=coco.yaml`
## Usage Examples
Train YOLO11n on the COCO8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) at image size 640. The training device can be specified using the `device` argument. If no argument is passed GPU `device=0` will be used if available, otherwise `device='cpu'` will be used. See Arguments section below for a full list of training arguments.
!!! example "Single-GPU and CPU Training Example"
Device is determined automatically. If a GPU is available then it will be used, otherwise training will start on CPU.
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.yaml") # build a new model from YAML
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.yaml").load("yolo11n.pt") # build from YAML and transfer weights
# Train the model
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Build a new model from YAML and start training from scratch
yolo detect train data=coco8.yaml model=yolo11n.yaml epochs=100 imgsz=640
# Start training from a pretrained *.pt model
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
# Build a new model from YAML, transfer pretrained weights to it and start training
yolo detect train data=coco8.yaml model=yolo11n.yaml pretrained=yolo11n.pt epochs=100 imgsz=640
```
### Multi-GPU Training
Multi-GPU training allows for more efficient utilization of available hardware resources by distributing the training load across multiple GPUs. This feature is available through both the Python API and the command-line interface. To enable multi-GPU training, specify the GPU device IDs you wish to use.
!!! example "Multi-GPU Training Example"
To train with 2 GPUs, CUDA devices 0 and 1 use the following commands. Expand to additional GPUs as required.
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model with 2 GPUs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640, device=[0, 1])
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model using GPUs 0 and 1
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640 device=0,1
```
### Apple Silicon MPS Training
With the support for Apple silicon chips integrated in the Ultralytics YOLO models, it's now possible to train your models on devices utilizing the powerful Metal Performance Shaders (MPS) framework. The MPS offers a high-performance way of executing computation and image processing tasks on Apple's custom silicon.
To enable training on Apple silicon chips, you should specify 'mps' as your device when initiating the training process. Below is an example of how you could do this in Python and via the command line:
!!! example "MPS Training Example"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model with MPS
results = model.train(data="coco8.yaml", epochs=100, imgsz=640, device="mps")
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model using MPS
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640 device=mps
```
While leveraging the computational power of the Apple silicon chips, this enables more efficient processing of the training tasks. For more detailed guidance and advanced configuration options, please refer to the [PyTorch MPS documentation](https://pytorch.org/docs/stable/notes/mps.html).
### Resuming Interrupted Trainings
Resuming training from a previously saved state is a crucial feature when working with deep learning models. This can come in handy in various scenarios, like when the training process has been unexpectedly interrupted, or when you wish to continue training a model with new data or for more epochs.
When training is resumed, Ultralytics YOLO loads the weights from the last saved model and also restores the optimizer state, [learning rate](https://www.ultralytics.com/glossary/learning-rate) scheduler, and the epoch number. This allows you to continue the training process seamlessly from where it was left off.
You can easily resume training in Ultralytics YOLO by setting the `resume` argument to `True` when calling the `train` method, and specifying the path to the `.pt` file containing the partially trained model weights.
Below is an example of how to resume an interrupted training using Python and via the command line:
!!! example "Resume Training Example"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("path/to/last.pt") # load a partially trained model
# Resume training
results = model.train(resume=True)
```
=== "CLI"
```bash
# Resume an interrupted training
yolo train resume model=path/to/last.pt
```
By setting `resume=True`, the `train` function will continue training from where it left off, using the state stored in the 'path/to/last.pt' file. If the `resume` argument is omitted or set to `False`, the `train` function will start a new training session.
Remember that checkpoints are saved at the end of every epoch by default, or at fixed intervals using the `save_period` argument, so you must complete at least 1 epoch to resume a training run.
## Train Settings
The training settings for YOLO models encompass various hyperparameters and configurations used during the training process. These settings influence the model's performance, speed, and [accuracy](https://www.ultralytics.com/glossary/accuracy). Key training settings include batch size, learning rate, momentum, and weight decay. Additionally, the choice of optimizer, [loss function](https://www.ultralytics.com/glossary/loss-function), and training dataset composition can impact the training process. Careful tuning and experimentation with these settings are crucial for optimizing performance.
{% include "macros/train-args.md" %}
!!! info "Note on Batch-size Settings"
The `batch` argument can be configured in three ways:
- **Fixed [Batch Size](https://www.ultralytics.com/glossary/batch-size)**: Set an integer value (e.g., `batch=16`), specifying the number of images per batch directly.
- **Auto Mode (60% GPU Memory)**: Use `batch=-1` to automatically adjust batch size for approximately 60% CUDA memory utilization.
- **Auto Mode with Utilization Fraction**: Set a fraction value (e.g., `batch=0.70`) to adjust batch size based on the specified fraction of GPU memory usage.
## Augmentation Settings and Hyperparameters
Augmentation techniques are essential for improving the robustness and performance of YOLO models by introducing variability into the [training data](https://www.ultralytics.com/glossary/training-data), helping the model generalize better to unseen data. The following table outlines the purpose and effect of each augmentation argument:
{% include "macros/augmentation-args.md" %}
These settings can be adjusted to meet the specific requirements of the dataset and task at hand. Experimenting with different values can help find the optimal augmentation strategy that leads to the best model performance.
!!! info
For more information about training augmentation operations, see the [reference section](../reference/data/augment.md).
## Logging
In training a YOLO11 model, you might find it valuable to keep track of the model's performance over time. This is where logging comes into play. Ultralytics' YOLO provides support for three types of loggers - Comet, ClearML, and TensorBoard.
To use a logger, select it from the dropdown menu in the code snippet above and run it. The chosen logger will be installed and initialized.
### Comet
[Comet](../integrations/comet.md) is a platform that allows data scientists and developers to track, compare, explain and optimize experiments and models. It provides functionalities such as real-time metrics, code diffs, and hyperparameters tracking.
To use Comet:
!!! example
=== "Python"
```python
# pip install comet_ml
import comet_ml
comet_ml.init()
```
Remember to sign in to your Comet account on their website and get your API key. You will need to add this to your environment variables or your script to log your experiments.
### ClearML
[ClearML](https://clear.ml/) is an open-source platform that automates tracking of experiments and helps with efficient sharing of resources. It is designed to help teams manage, execute, and reproduce their ML work more efficiently.
To use ClearML:
!!! example
=== "Python"
```python
# pip install clearml
import clearml
clearml.browser_login()
```
After running this script, you will need to sign in to your ClearML account on the browser and authenticate your session.
### TensorBoard
[TensorBoard](https://www.tensorflow.org/tensorboard) is a visualization toolkit for [TensorFlow](https://www.ultralytics.com/glossary/tensorflow). It allows you to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it.
To use TensorBoard in [Google Colab](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb):
!!! example
=== "CLI"
```bash
load_ext tensorboard
tensorboard --logdir ultralytics/runs # replace with 'runs' directory
```
To use TensorBoard locally run the below command and view results at http://localhost:6006/.
!!! example
=== "CLI"
```bash
tensorboard --logdir ultralytics/runs # replace with 'runs' directory
```
This will load TensorBoard and direct it to the directory where your training logs are saved.
After setting up your logger, you can then proceed with your model training. All training metrics will be automatically logged in your chosen platform, and you can access these logs to monitor your model's performance over time, compare different models, and identify areas for improvement.
## FAQ
### How do I train an [object detection](https://www.ultralytics.com/glossary/object-detection) model using Ultralytics YOLO11?
To train an object detection model using Ultralytics YOLO11, you can either use the Python API or the CLI. Below is an example for both:
!!! example "Single-GPU and CPU Training Example"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For more details, refer to the [Train Settings](#train-settings) section.
### What are the key features of Ultralytics YOLO11's Train mode?
The key features of Ultralytics YOLO11's Train mode include:
- **Automatic Dataset Download:** Automatically downloads standard datasets like COCO, VOC, and ImageNet.
- **Multi-GPU Support:** Scale training across multiple GPUs for faster processing.
- **Hyperparameter Configuration:** Customize hyperparameters through YAML files or CLI arguments.
- **Visualization and Monitoring:** Real-time tracking of training metrics for better insights.
These features make training efficient and customizable to your needs. For more details, see the [Key Features of Train Mode](#key-features-of-train-mode) section.
### How do I resume training from an interrupted session in Ultralytics YOLO11?
To resume training from an interrupted session, set the `resume` argument to `True` and specify the path to the last saved checkpoint.
!!! example "Resume Training Example"
=== "Python"
```python
from ultralytics import YOLO
# Load the partially trained model
model = YOLO("path/to/last.pt")
# Resume training
results = model.train(resume=True)
```
=== "CLI"
```bash
yolo train resume model=path/to/last.pt
```
Check the section on [Resuming Interrupted Trainings](#resuming-interrupted-trainings) for more information.
### Can I train YOLO11 models on Apple silicon chips?
Yes, Ultralytics YOLO11 supports training on Apple silicon chips utilizing the Metal Performance Shaders (MPS) framework. Specify 'mps' as your training device.
!!! example "MPS Training Example"
=== "Python"
```python
from ultralytics import YOLO
# Load a pretrained model
model = YOLO("yolo11n.pt")
# Train the model on Apple silicon chip (M1/M2/M3/M4)
results = model.train(data="coco8.yaml", epochs=100, imgsz=640, device="mps")
```
=== "CLI"
```bash
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640 device=mps
```
For more details, refer to the [Apple Silicon MPS Training](#apple-silicon-mps-training) section.
### What are the common training settings, and how do I configure them?
Ultralytics YOLO11 allows you to configure a variety of training settings such as batch size, learning rate, epochs, and more through arguments. Here's a brief overview:
| Argument | Default | Description |
| -------- | ------- | ---------------------------------------------------------------------- |
| `model` | `None` | Path to the model file for training. |
| `data` | `None` | Path to the dataset configuration file (e.g., `coco8.yaml`). |
| `epochs` | `100` | Total number of training epochs. |
| `batch` | `16` | Batch size, adjustable as integer or auto mode. |
| `imgsz` | `640` | Target image size for training. |
| `device` | `None` | Computational device(s) for training like `cpu`, `0`, `0,1`, or `mps`. |
| `save` | `True` | Enables saving of training checkpoints and final model weights. |
For an in-depth guide on training settings, check the [Train Settings](#train-settings) section.
docs/en/modes/val.md 0 → 100644
View file @ af155c51
---
comments: true
description: Learn how to validate your YOLO11 model with precise metrics, easy-to-use tools, and custom settings for optimal performance.
keywords: Ultralytics, YOLO11, model validation, machine learning, object detection, mAP metrics, Python API, CLI
---
# Model Validation with Ultralytics YOLO
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif" alt="Ultralytics YOLO ecosystem and integrations">
## Introduction
Validation is a critical step in the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) pipeline, allowing you to assess the quality of your trained models. Val mode in Ultralytics YOLO11 provides a robust suite of tools and metrics for evaluating the performance of your [object detection](https://www.ultralytics.com/glossary/object-detection) models. This guide serves as a complete resource for understanding how to effectively use the Val mode to ensure that your models are both accurate and reliable.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/j8uQc0qB91s?start=47"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Ultralytics Modes Tutorial: Validation
</p>
## Why Validate with Ultralytics YOLO?
Here's why using YOLO11's Val mode is advantageous:
- **Precision:** Get accurate metrics like mAP50, mAP75, and mAP50-95 to comprehensively evaluate your model.
- **Convenience:** Utilize built-in features that remember training settings, simplifying the validation process.
- **Flexibility:** Validate your model with the same or different datasets and image sizes.
- **[Hyperparameter Tuning](https://www.ultralytics.com/glossary/hyperparameter-tuning):** Use validation metrics to fine-tune your model for better performance.
### Key Features of Val Mode
These are the notable functionalities offered by YOLO11's Val mode:
- **Automated Settings:** Models remember their training configurations for straightforward validation.
- **Multi-Metric Support:** Evaluate your model based on a range of accuracy metrics.
- **CLI and Python API:** Choose from command-line interface or Python API based on your preference for validation.
- **Data Compatibility:** Works seamlessly with datasets used during the training phase as well as custom datasets.
!!! tip
* YOLO11 models automatically remember their training settings, so you can validate a model at the same image size and on the original dataset easily with just `yolo val model=yolo11n.pt` or `model('yolo11n.pt').val()`
## Usage Examples
Validate trained YOLO11n model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Validate the model
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.box.map # map50-95
metrics.box.map50 # map50
metrics.box.map75 # map75
metrics.box.maps # a list contains map50-95 of each category
```
=== "CLI"
```bash
yolo detect val model=yolo11n.pt # val official model
yolo detect val model=path/to/best.pt # val custom model
```
## Arguments for YOLO Model Validation
When validating YOLO models, several arguments can be fine-tuned to optimize the evaluation process. These arguments control aspects such as input image size, batch processing, and performance thresholds. Below is a detailed breakdown of each argument to help you customize your validation settings effectively.
{% include "macros/validation-args.md" %}
Each of these settings plays a vital role in the validation process, allowing for a customizable and efficient evaluation of YOLO models. Adjusting these parameters according to your specific needs and resources can help achieve the best balance between accuracy and performance.
### Example Validation with Arguments
The below examples showcase YOLO model validation with custom arguments in Python and CLI.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt")
# Customize validation settings
validation_results = model.val(data="coco8.yaml", imgsz=640, batch=16, conf=0.25, iou=0.6, device="0")
```
=== "CLI"
```bash
yolo val model=yolo11n.pt data=coco8.yaml imgsz=640 batch=16 conf=0.25 iou=0.6 device=0
```
## FAQ
### How do I validate my YOLO11 model with Ultralytics?
To validate your YOLO11 model, you can use the Val mode provided by Ultralytics. For example, using the Python API, you can load a model and run validation with:
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt")
# Validate the model
metrics = model.val()
print(metrics.box.map) # map50-95
```
Alternatively, you can use the command-line interface (CLI):
```bash
yolo val model=yolo11n.pt
```
For further customization, you can adjust various arguments like `imgsz`, `batch`, and `conf` in both Python and CLI modes. Check the [Arguments for YOLO Model Validation](#arguments-for-yolo-model-validation) section for the full list of parameters.
### What metrics can I get from YOLO11 model validation?
YOLO11 model validation provides several key metrics to assess model performance. These include:
- mAP50 (mean Average Precision at IoU threshold 0.5)
- mAP75 (mean Average Precision at IoU threshold 0.75)
- mAP50-95 (mean Average Precision across multiple IoU thresholds from 0.5 to 0.95)
Using the Python API, you can access these metrics as follows:
```python
metrics = model.val() # assumes `model` has been loaded
print(metrics.box.map) # mAP50-95
print(metrics.box.map50) # mAP50
print(metrics.box.map75) # mAP75
print(metrics.box.maps) # list of mAP50-95 for each category
```
For a complete performance evaluation, it's crucial to review all these metrics. For more details, refer to the [Key Features of Val Mode](#key-features-of-val-mode).
### What are the advantages of using Ultralytics YOLO for validation?
Using Ultralytics YOLO for validation provides several advantages:
- **[Precision](https://www.ultralytics.com/glossary/precision):** YOLO11 offers accurate performance metrics including mAP50, mAP75, and mAP50-95.
- **Convenience:** The models remember their training settings, making validation straightforward.
- **Flexibility:** You can validate against the same or different datasets and image sizes.
- **Hyperparameter Tuning:** Validation metrics help in fine-tuning models for better performance.
These benefits ensure that your models are evaluated thoroughly and can be optimized for superior results. Learn more about these advantages in the [Why Validate with Ultralytics YOLO](#why-validate-with-ultralytics-yolo) section.
### Can I validate my YOLO11 model using a custom dataset?
Yes, you can validate your YOLO11 model using a [custom dataset](https://docs.ultralytics.com/datasets/). Specify the `data` argument with the path to your dataset configuration file. This file should include paths to the [validation data](https://www.ultralytics.com/glossary/validation-data), class names, and other relevant details.
Example in Python:
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt")
# Validate with a custom dataset
metrics = model.val(data="path/to/your/custom_dataset.yaml")
print(metrics.box.map) # map50-95
```
Example using CLI:
```bash
yolo val model=yolo11n.pt data=path/to/your/custom_dataset.yaml
```
For more customizable options during validation, see the [Example Validation with Arguments](#example-validation-with-arguments) section.
### How do I save validation results to a JSON file in YOLO11?
To save the validation results to a JSON file, you can set the `save_json` argument to `True` when running validation. This can be done in both the Python API and CLI.
Example in Python:
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt")
# Save validation results to JSON
metrics = model.val(save_json=True)
```
Example using CLI:
```bash
yolo val model=yolo11n.pt save_json=True
```
This functionality is particularly useful for further analysis or integration with other tools. Check the [Arguments for YOLO Model Validation](#arguments-for-yolo-model-validation) for more details.
docs/en/quickstart.md 0 → 100644
View file @ af155c51
---
comments: true
description: Learn how to install Ultralytics using pip, conda, or Docker. Follow our step-by-step guide for a seamless setup of YOLO with thorough instructions.
keywords: Ultralytics, YOLO11, Install Ultralytics, pip, conda, Docker, GitHub, machine learning, object detection
---
## Install Ultralytics
Ultralytics provides various installation methods including pip, conda, and Docker. Install YOLO via the `ultralytics` pip package for the latest stable release or by cloning the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics) for the most up-to-date version. Docker can be used to execute the package in an isolated container, avoiding local installation.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/_a7cVL9hqnk"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Ultralytics YOLO Quick Start Guide
</p>
!!! example "Install"
<p align="left" style="margin-bottom: -20px;">![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)<p>
=== "Pip install (recommended)"
Install the `ultralytics` package using pip, or update an existing installation by running `pip install -U ultralytics`. Visit the Python Package Index (PyPI) for more details on the `ultralytics` package: [https://pypi.org/project/ultralytics/](https://pypi.org/project/ultralytics/).
[![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/)
[![Downloads](https://static.pepy.tech/badge/ultralytics)](https://www.pepy.tech/projects/ultralytics)
```bash
# Install the ultralytics package from PyPI
pip install ultralytics
```
You can also install the `ultralytics` package directly from the GitHub [repository](https://github.com/ultralytics/ultralytics). This might be useful if you want the latest development version. Make sure to have the Git command-line tool installed on your system. The `@main` command installs the `main` branch and may be modified to another branch, i.e. `@my-branch`, or removed entirely to default to `main` branch.
```bash
# Install the ultralytics package from GitHub
pip install git+https://github.com/ultralytics/ultralytics.git@main
```
=== "Conda install"
Conda is an alternative package manager to pip which may also be used for installation. Visit Anaconda for more details at [https://anaconda.org/conda-forge/ultralytics](https://anaconda.org/conda-forge/ultralytics). Ultralytics feedstock repository for updating the conda package is at [https://github.com/conda-forge/ultralytics-feedstock/](https://github.com/conda-forge/ultralytics-feedstock/).
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/ultralytics?logo=condaforge)](https://anaconda.org/conda-forge/ultralytics)
[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/ultralytics.svg)](https://anaconda.org/conda-forge/ultralytics)
[![Conda Recipe](https://img.shields.io/badge/recipe-ultralytics-green.svg)](https://anaconda.org/conda-forge/ultralytics)
[![Conda Platforms](https://img.shields.io/conda/pn/conda-forge/ultralytics.svg)](https://anaconda.org/conda-forge/ultralytics)
```bash
# Install the ultralytics package using conda
conda install -c conda-forge ultralytics
```
!!! note
If you are installing in a CUDA environment best practice is to install `ultralytics`, `pytorch` and `pytorch-cuda` in the same command to allow the conda package manager to resolve any conflicts, or else to install `pytorch-cuda` last to allow it override the CPU-specific `pytorch` package if necessary.
```bash
# Install all packages together using conda
conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics
```
### Conda Docker Image
Ultralytics Conda Docker images are also available from [DockerHub](https://hub.docker.com/r/ultralytics/ultralytics). These images are based on [Miniconda3](https://docs.conda.io/projects/miniconda/en/latest/) and are an simple way to start using `ultralytics` in a Conda environment.
```bash
# Set image name as a variable
t=ultralytics/ultralytics:latest-conda
# Pull the latest ultralytics image from Docker Hub
sudo docker pull $t
# Run the ultralytics image in a container with GPU support
sudo docker run -it --ipc=host --gpus all $t # all GPUs
sudo docker run -it --ipc=host --gpus '"device=2,3"' $t # specify GPUs
```
=== "Git clone"
Clone the `ultralytics` repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package in editable mode `-e` using pip.
[![GitHub last commit](https://img.shields.io/github/last-commit/ultralytics/ultralytics?logo=github)](https://github.com/ultralytics/ultralytics)
[![GitHub commit activity](https://img.shields.io/github/commit-activity/t/ultralytics/ultralytics)](https://github.com/ultralytics/ultralytics)
```bash
# Clone the ultralytics repository
git clone https://github.com/ultralytics/ultralytics
# Navigate to the cloned directory
cd ultralytics
# Install the package in editable mode for development
pip install -e .
```
=== "Docker"
Utilize Docker to effortlessly execute the `ultralytics` package in an isolated container, ensuring consistent and smooth performance across various environments. By choosing one of the official `ultralytics` images from [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics), you not only avoid the complexity of local installation but also benefit from access to a verified working environment. Ultralytics offers 5 main supported Docker images, each designed to provide high compatibility and efficiency for different platforms and use cases:
[![Docker Image Version](https://img.shields.io/docker/v/ultralytics/ultralytics?sort=semver&logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics)
[![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/ultralytics)](https://hub.docker.com/r/ultralytics/ultralytics)
- **Dockerfile:** GPU image recommended for training.
- **Dockerfile-arm64:** Optimized for ARM64 architecture, allowing deployment on devices like Raspberry Pi and other ARM64-based platforms.
- **Dockerfile-cpu:** Ubuntu-based CPU-only version suitable for inference and environments without GPUs.
- **Dockerfile-jetson:** Tailored for NVIDIA Jetson devices, integrating GPU support optimized for these platforms.
- **Dockerfile-python:** Minimal image with just Python and necessary dependencies, ideal for lightweight applications and development.
- **Dockerfile-conda:** Based on Miniconda3 with conda installation of ultralytics package.
Below are the commands to get the latest image and execute it:
```bash
# Set image name as a variable
t=ultralytics/ultralytics:latest
# Pull the latest ultralytics image from Docker Hub
sudo docker pull $t
# Run the ultralytics image in a container with GPU support
sudo docker run -it --ipc=host --gpus all $t # all GPUs
sudo docker run -it --ipc=host --gpus '"device=2,3"' $t # specify GPUs
```
The above command initializes a Docker container with the latest `ultralytics` image. The `-it` flag assigns a pseudo-TTY and maintains stdin open, enabling you to interact with the container. The `--ipc=host` flag sets the IPC (Inter-Process Communication) namespace to the host, which is essential for sharing memory between processes. The `--gpus all` flag enables access to all available GPUs inside the container, which is crucial for tasks that require GPU computation.
Note: To work with files on your local machine within the container, use Docker volumes for mounting a local directory into the container:
```bash
# Mount local directory to a directory inside the container
sudo docker run -it --ipc=host --gpus all -v /path/on/host:/path/in/container $t
```
Alter `/path/on/host` with the directory path on your local machine, and `/path/in/container` with the desired path inside the Docker container for accessibility.
For advanced Docker usage, feel free to explore the [Ultralytics Docker Guide](./guides/docker-quickstart.md).
See the `ultralytics` [pyproject.toml](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) file for a list of dependencies. Note that all examples above install all required dependencies.
!!! tip
[PyTorch](https://www.ultralytics.com/glossary/pytorch) requirements vary by operating system and CUDA requirements, so it's recommended to install PyTorch first following instructions at [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally/).
<a href="https://pytorch.org/get-started/locally/">
<img width="800" alt="PyTorch Installation Instructions" src="https://github.com/ultralytics/docs/releases/download/0/pytorch-installation-instructions.avif">
</a>
## Use Ultralytics with CLI
The Ultralytics command line interface (CLI) allows for simple single-line commands without the need for a Python environment. CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `yolo` command. Check out the [CLI Guide](usage/cli.md) to learn more about using YOLO from the command line.
!!! example
=== "Syntax"
Ultralytics `yolo` commands use the following syntax:
```bash
yolo TASK MODE ARGS
```
- `TASK` (optional) is one of ([detect](tasks/detect.md), [segment](tasks/segment.md), [classify](tasks/classify.md), [pose](tasks/pose.md), [obb](tasks/obb.md))
- `MODE` (required) is one of ([train](modes/train.md), [val](modes/val.md), [predict](modes/predict.md), [export](modes/export.md), [track](modes/track.md), [benchmark](modes/benchmark.md))
- `ARGS` (optional) are `arg=value` pairs like `imgsz=640` that override defaults.
See all `ARGS` in the full [Configuration Guide](usage/cfg.md) or with the `yolo cfg` CLI command.
=== "Train"
Train a detection model for 10 [epochs](https://www.ultralytics.com/glossary/epoch) with an initial learning_rate of 0.01
```bash
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
```
=== "Predict"
Predict a YouTube video using a pretrained segmentation model at image size 320:
```bash
yolo predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
```
=== "Val"
Val a pretrained detection model at batch-size 1 and image size 640:
```bash
yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640
```
=== "Export"
Export a yolo11n classification model to ONNX format at image size 224 by 128 (no TASK required)
```bash
yolo export model=yolo11n-cls.pt format=onnx imgsz=224,128
```
=== "Special"
Run special commands to see version, view settings, run checks and more:
```bash
yolo help
yolo checks
yolo version
yolo settings
yolo copy-cfg
yolo cfg
```
!!! warning
Arguments must be passed as `arg=val` pairs, split by an equals `=` sign and delimited by spaces between pairs. Do not use `--` argument prefixes or commas `,` between arguments.
- `yolo predict model=yolo11n.pt imgsz=640 conf=0.25` ✅
- `yolo predict model yolo11n.pt imgsz 640 conf 0.25` ❌ (missing `=`)
- `yolo predict model=yolo11n.pt, imgsz=640, conf=0.25` ❌ (do not use `,`)
- `yolo predict --model yolo11n.pt --imgsz 640 --conf 0.25` ❌ (do not use `--`)
[CLI Guide](usage/cli.md){ .md-button }
## Use Ultralytics with Python
YOLO's Python interface allows for seamless integration into your Python projects, making it easy to load, run, and process the model's output. Designed with simplicity and ease of use in mind, the Python interface enables users to quickly implement [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification in their projects. This makes YOLO's Python interface an invaluable tool for anyone looking to incorporate these functionalities into their Python projects.
For example, users can load a model, train it, evaluate its performance on a validation set, and even export it to ONNX format with just a few lines of code. Check out the [Python Guide](usage/python.md) to learn more about using YOLO within your Python projects.
!!! example
```python
from ultralytics import YOLO
# Create a new YOLO model from scratch
model = YOLO("yolo11n.yaml")
# Load a pretrained YOLO model (recommended for training)
model = YOLO("yolo11n.pt")
# Train the model using the 'coco8.yaml' dataset for 3 epochs
results = model.train(data="coco8.yaml", epochs=3)
# Evaluate the model's performance on the validation set
results = model.val()
# Perform object detection on an image using the model
results = model("https://ultralytics.com/images/bus.jpg")
# Export the model to ONNX format
success = model.export(format="onnx")
```
[Python Guide](usage/python.md){.md-button .md-button--primary}
## Ultralytics Settings
The Ultralytics library provides a powerful settings management system to enable fine-grained control over your experiments. By making use of the `SettingsManager` housed within the `ultralytics.utils` module, users can readily access and alter their settings. These are stored in a JSON file in the environment user configuration directory, and can be viewed or modified directly within the Python environment or via the Command-Line Interface (CLI).
### Inspecting Settings
To gain insight into the current configuration of your settings, you can view them directly:
!!! example "View settings"
=== "Python"
You can use Python to view your settings. Start by importing the `settings` object from the `ultralytics` module. Print and return settings using the following commands:
```python
from ultralytics import settings
# View all settings
print(settings)
# Return a specific setting
value = settings["runs_dir"]
```
=== "CLI"
Alternatively, the command-line interface allows you to check your settings with a simple command:
```bash
yolo settings
```
### Modifying Settings
Ultralytics allows users to easily modify their settings. Changes can be performed in the following ways:
!!! example "Update settings"
=== "Python"
Within the Python environment, call the `update` method on the `settings` object to change your settings:
```python
from ultralytics import settings
# Update a setting
settings.update({"runs_dir": "/path/to/runs"})
# Update multiple settings
settings.update({"runs_dir": "/path/to/runs", "tensorboard": False})
# Reset settings to default values
settings.reset()
```
=== "CLI"
If you prefer using the command-line interface, the following commands will allow you to modify your settings:
```bash
# Update a setting
yolo settings runs_dir='/path/to/runs'
# Update multiple settings
yolo settings runs_dir='/path/to/runs' tensorboard=False
# Reset settings to default values
yolo settings reset
```
### Understanding Settings
The table below provides an overview of the settings available for adjustment within Ultralytics. Each setting is outlined along with an example value, the data type, and a brief description.
| Name | Example Value | Data Type | Description |
| ------------------ | --------------------- | --------- | ----------------------------------------------------------------------------------------------------------------- |
| `settings_version` | `'0.0.4'` | `str` | Ultralytics _settings_ version (different from Ultralytics [pip] version) |
| `datasets_dir` | `'/path/to/datasets'` | `str` | The directory where the datasets are stored |
| `weights_dir` | `'/path/to/weights'` | `str` | The directory where the model weights are stored |
| `runs_dir` | `'/path/to/runs'` | `str` | The directory where the experiment runs are stored |
| `uuid` | `'a1b2c3d4'` | `str` | The unique identifier for the current settings |
| `sync` | `True` | `bool` | Whether to sync analytics and crashes to HUB |
| `api_key` | `''` | `str` | Ultralytics HUB [API Key] |
| `clearml` | `True` | `bool` | Whether to use [ClearML] logging |
| `comet` | `True` | `bool` | Whether to use [Comet ML] for experiment tracking and visualization |
| `dvc` | `True` | `bool` | Whether to use [DVC for experiment tracking] and version control |
| `hub` | `True` | `bool` | Whether to use [Ultralytics HUB] integration |
| `mlflow` | `True` | `bool` | Whether to use [MLFlow] for experiment tracking |
| `neptune` | `True` | `bool` | Whether to use [Neptune] for experiment tracking |
| `raytune` | `True` | `bool` | Whether to use [Ray Tune] for [hyperparameter tuning](https://www.ultralytics.com/glossary/hyperparameter-tuning) |
| `tensorboard` | `True` | `bool` | Whether to use [TensorBoard] for visualization |
| `wandb` | `True` | `bool` | Whether to use [Weights & Biases] logging |
| `vscode_msg` | `True` | `bool` | When VS Code terminal detected, enables prompt to download [Ultralytics-Snippets] extension. |
As you navigate through your projects or experiments, be sure to revisit these settings to ensure that they are optimally configured for your needs.
## FAQ
### How do I install Ultralytics using pip?
To install Ultralytics with pip, execute the following command:
```bash
pip install ultralytics
```
For the latest stable release, this will install the `ultralytics` package directly from the Python Package Index (PyPI). For more details, visit the [ultralytics package on PyPI](https://pypi.org/project/ultralytics/).
Alternatively, you can install the latest development version directly from GitHub:
```bash
pip install git+https://github.com/ultralytics/ultralytics.git
```
Make sure to have the Git command-line tool installed on your system.
### Can I install Ultralytics YOLO using conda?
Yes, you can install Ultralytics YOLO using conda by running:
```bash
conda install -c conda-forge ultralytics
```
This method is an excellent alternative to pip and ensures compatibility with other packages in your environment. For CUDA environments, it's best to install `ultralytics`, `pytorch`, and `pytorch-cuda` simultaneously to resolve any conflicts:
```bash
conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics
```
For more instructions, visit the [Conda quickstart guide](guides/conda-quickstart.md).
### What are the advantages of using Docker to run Ultralytics YOLO?
Using Docker to run Ultralytics YOLO provides an isolated and consistent environment, ensuring smooth performance across different systems. It also eliminates the complexity of local installation. Official Docker images from Ultralytics are available on [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics), with different variants tailored for GPU, CPU, ARM64, NVIDIA Jetson, and Conda environments. Below are the commands to pull and run the latest image:
```bash
# Pull the latest ultralytics image from Docker Hub
sudo docker pull ultralytics/ultralytics:latest
# Run the ultralytics image in a container with GPU support
sudo docker run -it --ipc=host --gpus all ultralytics/ultralytics:latest
```
For more detailed Docker instructions, check out the [Docker quickstart guide](guides/docker-quickstart.md).
### How do I clone the Ultralytics repository for development?
To clone the Ultralytics repository and set up a development environment, use the following steps:
```bash
# Clone the ultralytics repository
git clone https://github.com/ultralytics/ultralytics
# Navigate to the cloned directory
cd ultralytics
# Install the package in editable mode for development
pip install -e .
```
This approach allows you to contribute to the project or experiment with the latest source code. For more details, visit the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics).
### Why should I use Ultralytics YOLO CLI?
The Ultralytics YOLO command line interface (CLI) simplifies running object detection tasks without requiring Python code. You can execute single-line commands for tasks like training, validation, and prediction straight from your terminal. The basic syntax for `yolo` commands is:
```bash
yolo TASK MODE ARGS
```
For example, to train a detection model with specified parameters:
```bash
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
```
Check out the full [CLI Guide](usage/cli.md) to explore more commands and usage examples.
<!-- Article Links -->
[Ultralytics HUB]: https://hub.ultralytics.com
[API Key]: https://hub.ultralytics.com/settings?tab=api+keys
[pip]: https://pypi.org/project/ultralytics/
[DVC for experiment tracking]: https://dvc.org/doc/dvclive/ml-frameworks/yolo
[Comet ML]: https://bit.ly/yolov8-readme-comet
[Ultralytics HUB]: https://hub.ultralytics.com
[ClearML]: ./integrations/clearml.md
[MLFlow]: ./integrations/mlflow.md
[Neptune]: https://neptune.ai/
[Tensorboard]: ./integrations/tensorboard.md
[Ray Tune]: ./integrations/ray-tune.md
[Weights & Biases]: ./integrations/weights-biases.md
[Ultralytics-Snippets]: ./integrations/vscode.md
docs/en/reference/cfg/__init__.md 0 → 100644
View file @ af155c51
---
description: Explore the methods for managing and validating YOLO configurations in the Ultralytics configuration module. Enhance your YOLO experience.
keywords: Ultralytics, YOLO, configuration, cfg2dict, get_cfg, check_cfg, save_dir, deprecation, merge_args, yolo, settings, explorer
---
# Reference for `ultralytics/cfg/__init__.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/\_\_init\_\_.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/__init__.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/cfg/__init__.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.cfg.cfg2dict
<br><br><hr><br>
## ::: ultralytics.cfg.get_cfg
<br><br><hr><br>
## ::: ultralytics.cfg.check_cfg
<br><br><hr><br>
## ::: ultralytics.cfg.get_save_dir
<br><br><hr><br>
## ::: ultralytics.cfg._handle_deprecation
<br><br><hr><br>
## ::: ultralytics.cfg.check_dict_alignment
<br><br><hr><br>
## ::: ultralytics.cfg.merge_equals_args
<br><br><hr><br>
## ::: ultralytics.cfg.handle_yolo_hub
<br><br><hr><br>
## ::: ultralytics.cfg.handle_yolo_settings
<br><br><hr><br>
## ::: ultralytics.cfg.handle_yolo_solutions
<br><br><hr><br>
## ::: ultralytics.cfg.handle_streamlit_inference
<br><br><hr><br>
## ::: ultralytics.cfg.parse_key_value_pair
<br><br><hr><br>
## ::: ultralytics.cfg.smart_value
<br><br><hr><br>
## ::: ultralytics.cfg.entrypoint
<br><br><hr><br>
## ::: ultralytics.cfg.copy_default_cfg
<br><br>
docs/en/reference/data/annotator.md 0 → 100644
View file @ af155c51
---
description: Explore Ultralytics' annotator script for automatic image annotation using YOLO and SAM models. Contribute to improve it on GitHub!.
keywords: Ultralytics, image annotation, YOLO, SAM, Python script, GitHub, object detection, segmentation
---
# Reference for `ultralytics/data/annotator.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/annotator.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/annotator.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/data/annotator.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.data.annotator.auto_annotate
<br><br>
docs/en/reference/data/augment.md 0 → 100644
View file @ af155c51
---
description: Explore Ultralytics image augmentation techniques like MixUp, Mosaic, and Random Perspective for enhancing model training. Improve your deep learning models now.
keywords: Ultralytics, image augmentation, MixUp, Mosaic, Random Perspective, deep learning, model training, YOLO
---
# Reference for `ultralytics/data/augment.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/augment.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/augment.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/data/augment.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.data.augment.BaseTransform
<br><br><hr><br>
## ::: ultralytics.data.augment.Compose
<br><br><hr><br>
## ::: ultralytics.data.augment.BaseMixTransform
<br><br><hr><br>
## ::: ultralytics.data.augment.Mosaic
<br><br><hr><br>
## ::: ultralytics.data.augment.MixUp
<br><br><hr><br>
## ::: ultralytics.data.augment.RandomPerspective
<br><br><hr><br>
## ::: ultralytics.data.augment.RandomHSV
<br><br><hr><br>
## ::: ultralytics.data.augment.RandomFlip
<br><br><hr><br>
## ::: ultralytics.data.augment.LetterBox
<br><br><hr><br>
## ::: ultralytics.data.augment.CopyPaste
<br><br><hr><br>
## ::: ultralytics.data.augment.Albumentations
<br><br><hr><br>
## ::: ultralytics.data.augment.Format
<br><br><hr><br>
## ::: ultralytics.data.augment.RandomLoadText
<br><br><hr><br>
## ::: ultralytics.data.augment.ClassifyLetterBox
<br><br><hr><br>
## ::: ultralytics.data.augment.CenterCrop
<br><br><hr><br>
## ::: ultralytics.data.augment.ToTensor
<br><br><hr><br>
## ::: ultralytics.data.augment.v8_transforms
<br><br><hr><br>
## ::: ultralytics.data.augment.classify_transforms
<br><br><hr><br>
## ::: ultralytics.data.augment.classify_augmentations
<br><br>
docs/en/reference/data/base.md 0 → 100644
View file @ af155c51
---
description: Explore the Ultralytics BaseDataset class for efficient image loading and processing with custom transformations and caching options.
keywords: Ultralytics, BaseDataset, image processing, data augmentation, YOLO, dataset class, image caching
---
# Reference for `ultralytics/data/base.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/base.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/base.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/data/base.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.data.base.BaseDataset
<br><br>
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