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---
comments: true
description: Explore the YOLO11 command line interface (CLI) for easy execution of detection tasks without needing a Python environment.
keywords: YOLO11 CLI, command line interface, YOLO11 commands, detection tasks, Ultralytics, model training, model prediction
---
# Command Line Interface Usage
The YOLO 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.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/GsXGnb-A4Kc?start=19"
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> Mastering Ultralytics YOLO: CLI
</p>
!!! example
=== "Syntax"
Ultralytics `yolo` commands use the following syntax:
```bash
yolo TASK MODE ARGS
Where TASK (optional) is one of [detect, segment, classify, pose, obb]
MODE (required) is one of [train, val, predict, export, track, benchmark]
ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
```
See all ARGS in the full [Configuration Guide](cfg.md) or with `yolo cfg`
=== "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
```
Where:
- `TASK` (optional) is one of `[detect, segment, classify, pose, obb]`. If it is not passed explicitly YOLO11 will try to guess the `TASK` from the model type.
- `MODE` (required) is one of `[train, val, predict, export, track, benchmark]`
- `ARGS` (optional) are any number of custom `arg=value` pairs like `imgsz=320` that override defaults. For a full list of available `ARGS` see the [Configuration](cfg.md) page and `defaults.yaml`
!!! 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` &nbsp; ✅
- `yolo predict model yolo11n.pt imgsz 640 conf 0.25` &nbsp; ❌
- `yolo predict --model yolo11n.pt --imgsz 640 --conf 0.25` &nbsp; ❌
## Train
Train YOLO11n on the COCO8 dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](cfg.md) page.
!!! example
=== "Train"
Start training YOLO11n on COCO8 for 100 epochs at image-size 640.
```bash
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
```
=== "Resume"
Resume an interrupted training.
```bash
yolo detect train resume model=last.pt
```
## Val
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.
!!! example
=== "Official"
Validate an official YOLO11n model.
```bash
yolo detect val model=yolo11n.pt
```
=== "Custom"
Validate a custom-trained model.
```bash
yolo detect val model=path/to/best.pt
```
## Predict
Use a trained YOLO11n model to run predictions on images.
!!! example
=== "Official"
Predict with an official YOLO11n model.
```bash
yolo detect predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
```
=== "Custom"
Predict with a custom model.
```bash
yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg'
```
## Export
Export a YOLO11n model to a different format like ONNX, CoreML, etc.
!!! example
=== "Official"
Export an official YOLO11n model to ONNX format.
```bash
yolo export model=yolo11n.pt format=onnx
```
=== "Custom"
Export a custom-trained model to ONNX format.
```bash
yolo export model=path/to/best.pt format=onnx
```
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'`.
{% include "macros/export-table.md" %}
See full `export` details in the [Export](../modes/export.md) page.
## Overriding default arguments
Default arguments can be overridden by simply passing them as arguments in the CLI in `arg=value` pairs.
!!! tip
=== "Train"
Train a detection model for `10 epochs` with `learning_rate` of `0.01`
```bash
yolo detect 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 segment predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
```
=== "Val"
Validate a pretrained detection model at batch-size 1 and image size 640:
```bash
yolo detect val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640
```
## Overriding default config file
You can override the `default.yaml` config file entirely by passing a new file with the `cfg` arguments, i.e. `cfg=custom.yaml`.
To do this first create a copy of `default.yaml` in your current working dir with the `yolo copy-cfg` command.
This will create `default_copy.yaml`, which you can then pass as `cfg=default_copy.yaml` along with any additional args, like `imgsz=320` in this example:
!!! example
=== "CLI"
```bash
yolo copy-cfg
yolo cfg=default_copy.yaml imgsz=320
```
## FAQ
### How do I use the Ultralytics YOLO11 command line interface (CLI) for model training?
To train a YOLO11 model using the CLI, you can execute a simple one-line command in the terminal. For example, to train a detection model for 10 epochs with a [learning rate](https://www.ultralytics.com/glossary/learning-rate) of 0.01, you would run:
```bash
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
```
This command uses the `train` mode with specific arguments. Refer to the full list of available arguments in the [Configuration Guide](cfg.md).
### What tasks can I perform with the Ultralytics YOLO11 CLI?
The Ultralytics YOLO11 CLI supports a variety of tasks including detection, segmentation, classification, validation, prediction, export, and tracking. For instance:
- **Train a Model**: Run `yolo train data=<data.yaml> model=<model.pt> epochs=<num>`.
- **Run Predictions**: Use `yolo predict model=<model.pt> source=<data_source> imgsz=<image_size>`.
- **Export a Model**: Execute `yolo export model=<model.pt> format=<export_format>`.
Each task can be customized with various arguments. For detailed syntax and examples, see the respective sections like [Train](#train), [Predict](#predict), and [Export](#export).
### How can I validate the accuracy of a trained YOLO11 model using the CLI?
To validate a YOLO11 model's accuracy, use the `val` mode. For example, to validate a pretrained detection model with a [batch size](https://www.ultralytics.com/glossary/batch-size) of 1 and image size of 640, run:
```bash
yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640
```
This command evaluates the model on the specified dataset and provides performance metrics. For more details, refer to the [Val](#val) section.
### What formats can I export my YOLO11 models to using the CLI?
YOLO11 models can be exported to various formats such as ONNX, CoreML, TensorRT, and more. For instance, to export a model to ONNX format, run:
```bash
yolo export model=yolo11n.pt format=onnx
```
For complete details, visit the [Export](../modes/export.md) page.
### How do I customize YOLO11 CLI commands to override default arguments?
To override default arguments in YOLO11 CLI commands, pass them as `arg=value` pairs. For example, to train a model with custom arguments, use:
```bash
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
```
For a full list of available arguments and their descriptions, refer to the [Configuration Guide](cfg.md). Ensure arguments are formatted correctly, as shown in the [Overriding default arguments](#overriding-default-arguments) section.
docs/en/usage/engine.md 0 → 100644
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---
comments: true
description: Learn to customize the YOLO11 Trainer for specific tasks. Step-by-step instructions with Python examples for maximum model performance.
keywords: Ultralytics, YOLO11, Trainer Customization, Python, Machine Learning, AI, Model Training, DetectionTrainer, Custom Models
---
Both the Ultralytics YOLO command-line and Python interfaces are simply a high-level abstraction on the base engine executors. Let's take a look at the Trainer engine.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/GsXGnb-A4Kc?start=104"
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> Mastering Ultralytics YOLO: Advanced Customization
</p>
## BaseTrainer
BaseTrainer contains the generic boilerplate training routine. It can be customized for any task based over overriding the required functions or operations as long the as correct formats are followed. For example, you can support your own custom model and dataloader by just overriding these functions:
- `get_model(cfg, weights)` - The function that builds the model to be trained
- `get_dataloader()` - The function that builds the dataloader More details and source code can be found in [`BaseTrainer` Reference](../reference/engine/trainer.md)
## DetectionTrainer
Here's how you can use the YOLO11 `DetectionTrainer` and customize it.
```python
from ultralytics.models.yolo.detect import DetectionTrainer
trainer = DetectionTrainer(overrides={...})
trainer.train()
trained_model = trainer.best # get best model
```
### Customizing the DetectionTrainer
Let's customize the trainer **to train a custom detection model** that is not supported directly. You can do this by simply overloading the existing the `get_model` functionality:
```python
from ultralytics.models.yolo.detect import DetectionTrainer
class CustomTrainer(DetectionTrainer):
def get_model(self, cfg, weights):
"""Loads a custom detection model given configuration and weight files."""
...
trainer = CustomTrainer(overrides={...})
trainer.train()
```
You now realize that you need to customize the trainer further to:
- Customize the `loss function`.
- Add `callback` that uploads model to your Google Drive after every 10 `epochs` Here's how you can do it:
```python
from ultralytics.models.yolo.detect import DetectionTrainer
from ultralytics.nn.tasks import DetectionModel
class MyCustomModel(DetectionModel):
def init_criterion(self):
"""Initializes the loss function and adds a callback for uploading the model to Google Drive every 10 epochs."""
...
class CustomTrainer(DetectionTrainer):
def get_model(self, cfg, weights):
"""Returns a customized detection model instance configured with specified config and weights."""
return MyCustomModel(...)
# callback to upload model weights
def log_model(trainer):
"""Logs the path of the last model weight used by the trainer."""
last_weight_path = trainer.last
print(last_weight_path)
trainer = CustomTrainer(overrides={...})
trainer.add_callback("on_train_epoch_end", log_model) # Adds to existing callback
trainer.train()
```
To know more about Callback triggering events and entry point, checkout our [Callbacks Guide](callbacks.md)
## Other engine components
There are other components that can be customized similarly like `Validators` and `Predictors`. See Reference section for more information on these.
## FAQ
### How do I customize the Ultralytics YOLO11 DetectionTrainer for specific tasks?
To customize the Ultralytics YOLO11 `DetectionTrainer` for a specific task, you can override its methods to adapt to your custom model and dataloader. Start by inheriting from `DetectionTrainer` and then redefine methods like `get_model` to implement your custom functionalities. Here's an example:
```python
from ultralytics.models.yolo.detect import DetectionTrainer
class CustomTrainer(DetectionTrainer):
def get_model(self, cfg, weights):
"""Loads a custom detection model given configuration and weight files."""
...
trainer = CustomTrainer(overrides={...})
trainer.train()
trained_model = trainer.best # get best model
```
For further customization like changing the `loss function` or adding a `callback`, you can reference our [Callbacks Guide](../usage/callbacks.md).
### What are the key components of the BaseTrainer in Ultralytics YOLO11?
The `BaseTrainer` in Ultralytics YOLO11 serves as the foundation for training routines and can be customized for various tasks by overriding its generic methods. Key components include:
- `get_model(cfg, weights)` to build the model to be trained.
- `get_dataloader()` to build the dataloader.
For more details on the customization and source code, see the [`BaseTrainer` Reference](../reference/engine/trainer.md).
### How can I add a callback to the Ultralytics YOLO11 DetectionTrainer?
You can add callbacks to monitor and modify the training process in Ultralytics YOLO11 `DetectionTrainer`. For instance, here's how you can add a callback to log model weights after every training [epoch](https://www.ultralytics.com/glossary/epoch):
```python
from ultralytics.models.yolo.detect import DetectionTrainer
# callback to upload model weights
def log_model(trainer):
"""Logs the path of the last model weight used by the trainer."""
last_weight_path = trainer.last
print(last_weight_path)
trainer = DetectionTrainer(overrides={...})
trainer.add_callback("on_train_epoch_end", log_model) # Adds to existing callbacks
trainer.train()
```
For further details on callback events and entry points, refer to our [Callbacks Guide](../usage/callbacks.md).
### Why should I use Ultralytics YOLO11 for model training?
Ultralytics YOLO11 offers a high-level abstraction on powerful engine executors, making it ideal for rapid development and customization. Key benefits include:
- **Ease of Use**: Both command-line and Python interfaces simplify complex tasks.
- **Performance**: Optimized for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) and various vision AI applications.
- **Customization**: Easily extendable for custom models, [loss functions](https://www.ultralytics.com/glossary/loss-function), and dataloaders.
Learn more about YOLO11's capabilities by visiting [Ultralytics YOLO](https://www.ultralytics.com/yolo).
### Can I use the Ultralytics YOLO11 DetectionTrainer for non-standard models?
Yes, Ultralytics YOLO11 `DetectionTrainer` is highly flexible and can be customized for non-standard models. By inheriting from `DetectionTrainer`, you can overload different methods to support your specific model's needs. Here's a simple example:
```python
from ultralytics.models.yolo.detect import DetectionTrainer
class CustomDetectionTrainer(DetectionTrainer):
def get_model(self, cfg, weights):
"""Loads a custom detection model."""
...
trainer = CustomDetectionTrainer(overrides={...})
trainer.train()
```
For more comprehensive instructions and examples, review the [DetectionTrainer](../reference/engine/trainer.md) documentation.
docs/en/usage/python.md 0 → 100644
View file @ a74dc9a0
---
comments: true
description: Learn to integrate YOLO11 in Python for object detection, segmentation, and classification. Load, train models, and make predictions easily with our comprehensive guide.
keywords: YOLO11, Python, object detection, segmentation, classification, machine learning, AI, pretrained models, train models, make predictions
---
# Python Usage
Welcome to the YOLO11 Python Usage documentation! This guide is designed to help you seamlessly integrate YOLO11 into your Python projects for [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification. Here, you'll learn how to load and use pretrained models, train new models, and perform predictions on images. The easy-to-use Python interface is a valuable resource for anyone looking to incorporate YOLO11 into their Python projects, allowing you to quickly implement advanced object detection capabilities. Let's get started!
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/GsXGnb-A4Kc?start=58"
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> Mastering Ultralytics YOLO11: Python
</p>
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.
!!! example "Python"
```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")
```
## [Train](../modes/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.
!!! example "Train"
=== "From pretrained (recommended)"
```python
from ultralytics import YOLO
model = YOLO("yolo11n.pt") # pass any model type
results = model.train(epochs=5)
```
=== "From scratch"
```python
from ultralytics import YOLO
model = YOLO("yolo11n.yaml")
results = model.train(data="coco8.yaml", epochs=5)
```
=== "Resume"
```python
model = YOLO("last.pt")
results = model.train(resume=True)
```
[Train Examples](../modes/train.md){ .md-button }
## [Val](../modes/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](https://www.ultralytics.com/glossary/accuracy) and generalization performance. This mode can be used to tune the hyperparameters of the model to improve its performance.
!!! example "Val"
=== "Val after training"
```python
from ultralytics import YOLO
# Load a YOLO11 model
model = YOLO("yolo11n.yaml")
# Train the model
model.train(data="coco8.yaml", epochs=5)
# Validate on training data
model.val()
```
=== "Val on another dataset"
```python
from ultralytics import YOLO
# Load a YOLO11 model
model = YOLO("yolo11n.yaml")
# Train the model
model.train(data="coco8.yaml", epochs=5)
# Validate on separate data
model.val(data="path/to/separate/data.yaml")
```
[Val Examples](../modes/val.md){ .md-button }
## [Predict](../modes/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.
!!! example "Predict"
=== "From source"
```python
import cv2
from PIL import Image
from ultralytics import YOLO
model = YOLO("model.pt")
# accepts all formats - image/dir/Path/URL/video/PIL/ndarray. 0 for webcam
results = model.predict(source="0")
results = model.predict(source="folder", show=True) # Display preds. Accepts all YOLO predict arguments
# from PIL
im1 = Image.open("bus.jpg")
results = model.predict(source=im1, save=True) # save plotted images
# from ndarray
im2 = cv2.imread("bus.jpg")
results = model.predict(source=im2, save=True, save_txt=True) # save predictions as labels
# from list of PIL/ndarray
results = model.predict(source=[im1, im2])
```
=== "Results usage"
```python
# results would be a list of Results object including all the predictions by default
# but be careful as it could occupy a lot memory when there're many images,
# especially the task is segmentation.
# 1. return as a list
results = model.predict(source="folder")
# results would be a generator which is more friendly to memory by setting stream=True
# 2. return as a generator
results = model.predict(source=0, stream=True)
for result in results:
# Detection
result.boxes.xyxy # box with xyxy format, (N, 4)
result.boxes.xywh # box with xywh format, (N, 4)
result.boxes.xyxyn # box with xyxy format but normalized, (N, 4)
result.boxes.xywhn # box with xywh format but normalized, (N, 4)
result.boxes.conf # confidence score, (N, 1)
result.boxes.cls # cls, (N, 1)
# Segmentation
result.masks.data # masks, (N, H, W)
result.masks.xy # x,y segments (pixels), List[segment] * N
result.masks.xyn # x,y segments (normalized), List[segment] * N
# Classification
result.probs # cls prob, (num_class, )
# Each result is composed of torch.Tensor by default,
# in which you can easily use following functionality:
result = result.cuda()
result = result.cpu()
result = result.to("cpu")
result = result.numpy()
```
[Predict Examples](../modes/predict.md){ .md-button }
## [Export](../modes/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.
!!! example "Export"
=== "Export to ONNX"
Export an official YOLO11n model to ONNX with dynamic batch-size and image-size.
```python
from ultralytics import YOLO
model = YOLO("yolo11n.pt")
model.export(format="onnx", dynamic=True)
```
=== "Export to TensorRT"
Export an official YOLO11n model to TensorRT on `device=0` for acceleration on CUDA devices.
```python
from ultralytics import YOLO
model = YOLO("yolo11n.pt")
model.export(format="onnx", device=0)
```
[Export Examples](../modes/export.md){ .md-button }
## [Track](../modes/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.
!!! example "Track"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load an official detection model
model = YOLO("yolo11n-seg.pt") # load an official segmentation model
model = YOLO("path/to/best.pt") # load a custom model
# Track with the model
results = model.track(source="https://youtu.be/LNwODJXcvt4", show=True)
results = model.track(source="https://youtu.be/LNwODJXcvt4", show=True, tracker="bytetrack.yaml")
```
[Track Examples](../modes/track.md){ .md-button }
## [Benchmark](../modes/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 and segmentation) or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export 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.
!!! example "Benchmark"
=== "Python"
Benchmark an official YOLO11n model across all export formats.
```python
from ultralytics.utils.benchmarks import benchmark
# Benchmark
benchmark(model="yolo11n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
```
[Benchmark Examples](../modes/benchmark.md){ .md-button }
## Using Trainers
`YOLO` model class is a high-level wrapper on the Trainer classes. Each YOLO task has its own trainer that inherits from `BaseTrainer`.
!!! tip "Detection Trainer Example"
```python
from ultralytics.models.yolo import DetectionPredictor, DetectionTrainer, DetectionValidator
# trainer
trainer = DetectionTrainer(overrides={})
trainer.train()
trained_model = trainer.best
# Validator
val = DetectionValidator(args=...)
val(model=trained_model)
# predictor
pred = DetectionPredictor(overrides={})
pred(source=SOURCE, model=trained_model)
# resume from last weight
overrides["resume"] = trainer.last
trainer = detect.DetectionTrainer(overrides=overrides)
```
You can easily customize Trainers to support custom tasks or explore R&D ideas. Learn more about Customizing `Trainers`, `Validators` and `Predictors` to suit your project needs in the Customization Section.
[Customization tutorials](engine.md){ .md-button }
## FAQ
### How can I integrate YOLO11 into my Python project for object detection?
Integrating Ultralytics YOLO11 into your Python projects is simple. You can load a pre-trained model or train a new model from scratch. Here's how to get started:
```python
from ultralytics import YOLO
# Load a pretrained YOLO model
model = YOLO("yolo11n.pt")
# Perform object detection on an image
results = model("https://ultralytics.com/images/bus.jpg")
# Visualize the results
for result in results:
result.show()
```
See more detailed examples in our [Predict Mode](../modes/predict.md) section.
### What are the different modes available in YOLO11?
Ultralytics YOLO11 provides various modes to cater to different [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) workflows. These include:
- **[Train](../modes/train.md)**: Train a model using custom datasets.
- **[Val](../modes/val.md)**: Validate model performance on a validation set.
- **[Predict](../modes/predict.md)**: Make predictions on new images or video streams.
- **[Export](../modes/export.md)**: Export models to various formats like ONNX, TensorRT.
- **[Track](../modes/track.md)**: Real-time object tracking in video streams.
- **[Benchmark](../modes/benchmark.md)**: Benchmark model performance across different configurations.
Each mode is designed to provide comprehensive functionalities for different stages of model development and deployment.
### How do I train a custom YOLO11 model using my dataset?
To train a custom YOLO11 model, you need to specify your dataset and other hyperparameters. Here's a quick example:
```python
from ultralytics import YOLO
# Load the YOLO model
model = YOLO("yolo11n.yaml")
# Train the model with custom dataset
model.train(data="path/to/your/dataset.yaml", epochs=10)
```
For more details on training and hyperlinks to example usage, visit our [Train Mode](../modes/train.md) page.
### How do I export YOLO11 models for deployment?
Exporting YOLO11 models in a format suitable for deployment is straightforward with the `export` function. For example, you can export a model to ONNX format:
```python
from ultralytics import YOLO
# Load the YOLO model
model = YOLO("yolo11n.pt")
# Export the model to ONNX format
model.export(format="onnx")
```
For various export options, refer to the [Export Mode](../modes/export.md) documentation.
### Can I validate my YOLO11 model on different datasets?
Yes, validating YOLO11 models on different datasets is possible. After training, you can use the validation mode to evaluate the performance:
```python
from ultralytics import YOLO
# Load a YOLO11 model
model = YOLO("yolo11n.yaml")
# Train the model
model.train(data="coco8.yaml", epochs=5)
# Validate the model on a different dataset
model.val(data="path/to/separate/data.yaml")
```
Check the [Val Mode](../modes/val.md) page for detailed examples and usage.
docs/en/usage/simple-utilities.md 0 → 100644
View file @ a74dc9a0
---
comments: true
description: Explore essential utilities in the Ultralytics package to speed up and enhance your workflows. Learn about data processing, annotations, conversions, and more.
keywords: Ultralytics, utilities, data processing, auto annotation, YOLO, dataset conversion, bounding boxes, image compression, machine learning tools
---
# Simple Utilities
<p align="center">
<img src="https://github.com/ultralytics/docs/releases/download/0/code-with-perspective.avif" alt="code with perspective">
</p>
The `ultralytics` package comes with a myriad of utilities that can support, enhance, and speed up your workflows. There are many more available, but here are some that will be useful for most developers. They're also a great reference point to use when learning to program.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/1bPY2LRG590"
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 Utilities | Auto Annotation, Explorer API and Dataset Conversion
</p>
## Data
### Auto Labeling / Annotations
Dataset annotation is a very resource intensive and time-consuming process. If you have a YOLO [object detection](https://www.ultralytics.com/glossary/object-detection) model trained on a reasonable amount of data, you can use it and [SAM](../models/sam.md) to auto-annotate additional data (segmentation format).
```{ .py .annotate }
from ultralytics.data.annotator import auto_annotate
auto_annotate( # (1)!
data="path/to/new/data",
det_model="yolo11n.pt",
sam_model="mobile_sam.pt",
device="cuda",
output_dir="path/to/save_labels",
)
```
1. Nothing returns from this function
- [See the reference section for `annotator.auto_annotate`](../reference/data/annotator.md#ultralytics.data.annotator.auto_annotate) for more insight on how the function operates.
- Use in combination with the [function `segments2boxes`](#convert-segments-to-bounding-boxes) to generate object detection bounding boxes as well
### Convert Segmentation Masks into YOLO Format
![Segmentation Masks to YOLO Format](https://github.com/ultralytics/docs/releases/download/0/segmentation-masks-to-yolo-format.avif)
Use to convert a dataset of segmentation mask images to the `YOLO` segmentation format.
This function takes the directory containing the binary format mask images and converts them into YOLO segmentation format.
The converted masks will be saved in the specified output directory.
```python
from ultralytics.data.converter import convert_segment_masks_to_yolo_seg
# The classes here is the total classes in the dataset, for COCO dataset we have 80 classes
convert_segment_masks_to_yolo_seg(masks_dir="path/to/masks_dir", output_dir="path/to/output_dir", classes=80)
```
### Convert COCO into YOLO Format
Use to convert COCO JSON annotations into proper YOLO format. For object detection (bounding box) datasets, `use_segments` and `use_keypoints` should both be `False`
```{ .py .annotate }
from ultralytics.data.converter import convert_coco
convert_coco( # (1)!
"../datasets/coco/annotations/",
use_segments=False,
use_keypoints=False,
cls91to80=True,
)
```
1. Nothing returns from this function
For additional information about the `convert_coco` function, [visit the reference page](../reference/data/converter.md#ultralytics.data.converter.convert_coco)
### Get [Bounding Box](https://www.ultralytics.com/glossary/bounding-box) Dimensions
```{.py .annotate }
from ultralytics.utils.plotting import Annotator
from ultralytics import YOLO
import cv2
model = YOLO('yolo11n.pt') # Load pretrain or fine-tune model
# Process the image
source = cv2.imread('path/to/image.jpg')
results = model(source)
# Extract results
annotator = Annotator(source, example=model.names)
for box in results[0].boxes.xyxy.cpu():
width, height, area = annotator.get_bbox_dimension(box)
print("Bounding Box Width {}, Height {}, Area {}".format(
width.item(), height.item(), area.item()))
```
### Convert Bounding Boxes to Segments
With existing `x y w h` bounding box data, convert to segments using the `yolo_bbox2segment` function. The files for images and annotations need to be organized like this:
```
data
|__ images
├─ 001.jpg
├─ 002.jpg
├─ ..
└─ NNN.jpg
|__ labels
├─ 001.txt
├─ 002.txt
├─ ..
└─ NNN.txt
```
```{ .py .annotate }
from ultralytics.data.converter import yolo_bbox2segment
yolo_bbox2segment( # (1)!
im_dir="path/to/images",
save_dir=None, # saved to "labels-segment" in images directory
sam_model="sam_b.pt",
)
```
1. Nothing returns from this function
[Visit the `yolo_bbox2segment` reference page](../reference/data/converter.md#ultralytics.data.converter.yolo_bbox2segment) for more information regarding the function.
### Convert Segments to Bounding Boxes
If you have a dataset that uses the [segmentation dataset format](../datasets/segment/index.md) you can easily convert these into up-right (or horizontal) bounding boxes (`x y w h` format) with this function.
```python
import numpy as np
from ultralytics.utils.ops import segments2boxes
segments = np.array(
[
[805, 392, 797, 400, ..., 808, 714, 808, 392],
[115, 398, 113, 400, ..., 150, 400, 149, 298],
[267, 412, 265, 413, ..., 300, 413, 299, 412],
]
)
segments2boxes([s.reshape(-1, 2) for s in segments])
# >>> array([[ 741.66, 631.12, 133.31, 479.25],
# [ 146.81, 649.69, 185.62, 502.88],
# [ 281.81, 636.19, 118.12, 448.88]],
# dtype=float32) # xywh bounding boxes
```
To understand how this function works, visit the [reference page](../reference/utils/ops.md#ultralytics.utils.ops.segments2boxes)
## Utilities
### Image Compression
Compresses a single image file to reduced size while preserving its aspect ratio and quality. If the input image is smaller than the maximum dimension, it will not be resized.
```{ .py .annotate }
from pathlib import Path
from ultralytics.data.utils import compress_one_image
for f in Path("path/to/dataset").rglob("*.jpg"):
compress_one_image(f) # (1)!
```
1. Nothing returns from this function
### Auto-split Dataset
Automatically split a dataset into `train`/`val`/`test` splits and save the resulting splits into `autosplit_*.txt` files. This function will use random sampling, which is not included when using [`fraction` argument for training](../modes/train.md#train-settings).
```{ .py .annotate }
from ultralytics.data.utils import autosplit
autosplit( # (1)!
path="path/to/images",
weights=(0.9, 0.1, 0.0), # (train, validation, test) fractional splits
annotated_only=False, # split only images with annotation file when True
)
```
1. Nothing returns from this function
See the [Reference page](../reference/data/utils.md#ultralytics.data.utils.autosplit) for additional details on this function.
### Segment-polygon to Binary Mask
Convert a single polygon (as list) to a binary mask of the specified image size. Polygon in the form of `[N, 2]` with `N` as the number of `(x, y)` points defining the polygon contour.
!!! warning
`N` <b><u>must always</b></u> be even.
```python
import numpy as np
from ultralytics.data.utils import polygon2mask
imgsz = (1080, 810)
polygon = np.array([805, 392, 797, 400, ..., 808, 714, 808, 392]) # (238, 2)
mask = polygon2mask(
imgsz, # tuple
[polygon], # input as list
color=255, # 8-bit binary
downsample_ratio=1,
)
```
## Bounding Boxes
### Bounding Box (horizontal) Instances
To manage bounding box data, the `Bboxes` class will help to convert between box coordinate formatting, scale box dimensions, calculate areas, include offsets, and more!
```python
import numpy as np
from ultralytics.utils.instance import Bboxes
boxes = Bboxes(
bboxes=np.array(
[
[22.878, 231.27, 804.98, 756.83],
[48.552, 398.56, 245.35, 902.71],
[669.47, 392.19, 809.72, 877.04],
[221.52, 405.8, 344.98, 857.54],
[0, 550.53, 63.01, 873.44],
[0.0584, 254.46, 32.561, 324.87],
]
),
format="xyxy",
)
boxes.areas()
# >>> array([ 4.1104e+05, 99216, 68000, 55772, 20347, 2288.5])
boxes.convert("xywh")
print(boxes.bboxes)
# >>> array(
# [[ 413.93, 494.05, 782.1, 525.56],
# [ 146.95, 650.63, 196.8, 504.15],
# [ 739.6, 634.62, 140.25, 484.85],
# [ 283.25, 631.67, 123.46, 451.74],
# [ 31.505, 711.99, 63.01, 322.91],
# [ 16.31, 289.67, 32.503, 70.41]]
# )
```
See the [`Bboxes` reference section](../reference/utils/instance.md#ultralytics.utils.instance.Bboxes) for more attributes and methods available.
!!! tip
Many of the following functions (and more) can be accessed using the [`Bboxes` class](#bounding-box-horizontal-instances) but if you prefer to work with the functions directly, see the next subsections on how to import these independently.
### Scaling Boxes
When scaling and image up or down, corresponding bounding box coordinates can be appropriately scaled to match using `ultralytics.utils.ops.scale_boxes`.
```{ .py .annotate }
import cv2 as cv
import numpy as np
from ultralytics.utils.ops import scale_boxes
image = cv.imread("ultralytics/assets/bus.jpg")
h, w, c = image.shape
resized = cv.resize(image, None, (), fx=1.2, fy=1.2)
new_h, new_w, _ = resized.shape
xyxy_boxes = np.array(
[
[22.878, 231.27, 804.98, 756.83],
[48.552, 398.56, 245.35, 902.71],
[669.47, 392.19, 809.72, 877.04],
[221.52, 405.8, 344.98, 857.54],
[0, 550.53, 63.01, 873.44],
[0.0584, 254.46, 32.561, 324.87],
]
)
new_boxes = scale_boxes(
img1_shape=(h, w), # original image dimensions
boxes=xyxy_boxes, # boxes from original image
img0_shape=(new_h, new_w), # resized image dimensions (scale to)
ratio_pad=None,
padding=False,
xywh=False,
)
print(new_boxes) # (1)!
# >>> array(
# [[ 27.454, 277.52, 965.98, 908.2],
# [ 58.262, 478.27, 294.42, 1083.3],
# [ 803.36, 470.63, 971.66, 1052.4],
# [ 265.82, 486.96, 413.98, 1029],
# [ 0, 660.64, 75.612, 1048.1],
# [ 0.0701, 305.35, 39.073, 389.84]]
# )
```
1. Bounding boxes scaled for the new image size
### Bounding Box Format Conversions
#### XYXY → XYWH
Convert bounding box coordinates from (x1, y1, x2, y2) format to (x, y, width, height) format where (x1, y1) is the top-left corner and (x2, y2) is the bottom-right corner.
```python
import numpy as np
from ultralytics.utils.ops import xyxy2xywh
xyxy_boxes = np.array(
[
[22.878, 231.27, 804.98, 756.83],
[48.552, 398.56, 245.35, 902.71],
[669.47, 392.19, 809.72, 877.04],
[221.52, 405.8, 344.98, 857.54],
[0, 550.53, 63.01, 873.44],
[0.0584, 254.46, 32.561, 324.87],
]
)
xywh = xyxy2xywh(xyxy_boxes)
print(xywh)
# >>> array(
# [[ 413.93, 494.05, 782.1, 525.56],
# [ 146.95, 650.63, 196.8, 504.15],
# [ 739.6, 634.62, 140.25, 484.85],
# [ 283.25, 631.67, 123.46, 451.74],
# [ 31.505, 711.99, 63.01, 322.91],
# [ 16.31, 289.67, 32.503, 70.41]]
# )
```
### All Bounding Box Conversions
```python
from ultralytics.utils.ops import (
ltwh2xywh,
ltwh2xyxy,
xywh2ltwh, # xywh → top-left corner, w, h
xywh2xyxy,
xywhn2xyxy, # normalized → pixel
xyxy2ltwh, # xyxy → top-left corner, w, h
xyxy2xywhn, # pixel → normalized
)
for func in (ltwh2xywh, ltwh2xyxy, xywh2ltwh, xywh2xyxy, xywhn2xyxy, xyxy2ltwh, xyxy2xywhn):
print(help(func)) # print function docstrings
```
See docstring for each function or visit the `ultralytics.utils.ops` [reference page](../reference/utils/ops.md) to read more about each function.
## Plotting
### Drawing Annotations
Ultralytics includes an Annotator class that can be used to annotate any kind of data. It's easiest to use with [object detection bounding boxes](../modes/predict.md#boxes), [pose key points](../modes/predict.md#keypoints), and [oriented bounding boxes](../modes/predict.md#obb).
#### Horizontal Bounding Boxes
```{ .py .annotate }
import cv2 as cv
import numpy as np
from ultralytics.utils.plotting import Annotator, colors
names = { # (1)!
0: "person",
5: "bus",
11: "stop sign",
}
image = cv.imread("ultralytics/assets/bus.jpg")
ann = Annotator(
image,
line_width=None, # default auto-size
font_size=None, # default auto-size
font="Arial.ttf", # must be ImageFont compatible
pil=False, # use PIL, otherwise uses OpenCV
)
xyxy_boxes = np.array(
[
[5, 22.878, 231.27, 804.98, 756.83], # class-idx x1 y1 x2 y2
[0, 48.552, 398.56, 245.35, 902.71],
[0, 669.47, 392.19, 809.72, 877.04],
[0, 221.52, 405.8, 344.98, 857.54],
[0, 0, 550.53, 63.01, 873.44],
[11, 0.0584, 254.46, 32.561, 324.87],
]
)
for nb, box in enumerate(xyxy_boxes):
c_idx, *box = box
label = f"{str(nb).zfill(2)}:{names.get(int(c_idx))}"
ann.box_label(box, label, color=colors(c_idx, bgr=True))
image_with_bboxes = ann.result()
```
1. Names can be used from `model.names` when [working with detection results](../modes/predict.md#working-with-results)
#### Oriented Bounding Boxes (OBB)
```python
import cv2 as cv
import numpy as np
from ultralytics.utils.plotting import Annotator, colors
obb_names = {10: "small vehicle"}
obb_image = cv.imread("datasets/dota8/images/train/P1142__1024__0___824.jpg")
obb_boxes = np.array(
[
[0, 635, 560, 919, 719, 1087, 420, 803, 261], # class-idx x1 y1 x2 y2 x3 y2 x4 y4
[0, 331, 19, 493, 260, 776, 70, 613, -171],
[9, 869, 161, 886, 147, 851, 101, 833, 115],
]
)
ann = Annotator(
obb_image,
line_width=None, # default auto-size
font_size=None, # default auto-size
font="Arial.ttf", # must be ImageFont compatible
pil=False, # use PIL, otherwise uses OpenCV
)
for obb in obb_boxes:
c_idx, *obb = obb
obb = np.array(obb).reshape(-1, 4, 2).squeeze()
label = f"{obb_names.get(int(c_idx))}"
ann.box_label(
obb,
label,
color=colors(c_idx, True),
rotated=True,
)
image_with_obb = ann.result()
```
#### Bounding Boxes Circle Annotation [Circle Label](https://docs.ultralytics.com/reference/utils/plotting/#ultralytics.utils.plotting.Annotator.circle_label)
```python
import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator
model = YOLO("yolo11s.pt")
names = model.names
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
writer = cv2.VideoWriter("Ultralytics circle annotation.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))
while True:
ret, im0 = cap.read()
if not ret:
break
annotator = Annotator(im0)
results = model.predict(im0)
boxes = results[0].boxes.xyxy.cpu()
clss = results[0].boxes.cls.cpu().tolist()
for box, cls in zip(boxes, clss):
annotator.circle_label(box, label=names[int(cls)])
writer.write(im0)
cv2.imshow("Ultralytics circle annotation", im0)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
writer.release()
cap.release()
cv2.destroyAllWindows()
```
#### Bounding Boxes Text Annotation [Text Label](https://docs.ultralytics.com/reference/utils/plotting/#ultralytics.utils.plotting.Annotator.text_label)
```python
import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator
model = YOLO("yolo11s.pt")
names = model.names
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
writer = cv2.VideoWriter("Ultralytics text annotation.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))
while True:
ret, im0 = cap.read()
if not ret:
break
annotator = Annotator(im0)
results = model.predict(im0)
boxes = results[0].boxes.xyxy.cpu()
clss = results[0].boxes.cls.cpu().tolist()
for box, cls in zip(boxes, clss):
annotator.text_label(box, label=names[int(cls)])
writer.write(im0)
cv2.imshow("Ultralytics text annotation", im0)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
writer.release()
cap.release()
cv2.destroyAllWindows()
```
See the [`Annotator` Reference Page](../reference/utils/plotting.md#ultralytics.utils.plotting.Annotator) for additional insight.
## Miscellaneous
### Code Profiling
Check duration for code to run/process either using `with` or as a decorator.
```python
from ultralytics.utils.ops import Profile
with Profile(device="cuda:0") as dt:
pass # operation to measure
print(dt)
# >>> "Elapsed time is 9.5367431640625e-07 s"
```
### Ultralytics Supported Formats
Want or need to use the formats of [images or videos types supported](../modes/predict.md#image-and-video-formats) by Ultralytics programmatically? Use these constants if you need.
```python
from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS
print(IMG_FORMATS)
# {'tiff', 'pfm', 'bmp', 'mpo', 'dng', 'jpeg', 'png', 'webp', 'tif', 'jpg'}
print(VID_FORMATS)
# {'avi', 'mpg', 'wmv', 'mpeg', 'm4v', 'mov', 'mp4', 'asf', 'mkv', 'ts', 'gif', 'webm'}
```
### Make Divisible
Calculates the nearest whole number to `x` to make evenly divisible when divided by `y`.
```python
from ultralytics.utils.ops import make_divisible
make_divisible(7, 3)
# >>> 9
make_divisible(7, 2)
# >>> 8
```
## FAQ
### What utilities are included in the Ultralytics package to enhance [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) workflows?
The Ultralytics package includes a variety of utilities designed to streamline and optimize machine learning workflows. Key utilities include [auto-annotation](../reference/data/annotator.md#ultralytics.data.annotator.auto_annotate) for labeling datasets, converting COCO to YOLO format with [convert_coco](../reference/data/converter.md#ultralytics.data.converter.convert_coco), compressing images, and dataset auto-splitting. These tools aim to reduce manual effort, ensure consistency, and enhance data processing efficiency.
### How can I use Ultralytics to auto-label my dataset?
If you have a pre-trained Ultralytics YOLO object detection model, you can use it with the [SAM](../models/sam.md) model to auto-annotate your dataset in segmentation format. Here's an example:
```python
from ultralytics.data.annotator import auto_annotate
auto_annotate(
data="path/to/new/data",
det_model="yolo11n.pt",
sam_model="mobile_sam.pt",
device="cuda",
output_dir="path/to/save_labels",
)
```
For more details, check the [auto_annotate reference section](../reference/data/annotator.md#ultralytics.data.annotator.auto_annotate).
### How do I convert COCO dataset annotations to YOLO format in Ultralytics?
To convert COCO JSON annotations into YOLO format for object detection, you can use the `convert_coco` utility. Here's a sample code snippet:
```python
from ultralytics.data.converter import convert_coco
convert_coco(
"../datasets/coco/annotations/",
use_segments=False,
use_keypoints=False,
cls91to80=True,
)
```
For additional information, visit the [convert_coco reference page](../reference/data/converter.md#ultralytics.data.converter.convert_coco).
### What is the purpose of the YOLO Data Explorer in the Ultralytics package?
The [YOLO Explorer](../datasets/explorer/index.md) is a powerful tool introduced in the `8.1.0` update to enhance dataset understanding. It allows you to use text queries to find object instances in your dataset, making it easier to analyze and manage your data. This tool provides valuable insights into dataset composition and distribution, helping to improve model training and performance.
### How can I convert bounding boxes to segments in Ultralytics?
To convert existing bounding box data (in `x y w h` format) to segments, you can use the `yolo_bbox2segment` function. Ensure your files are organized with separate directories for images and labels.
```python
from ultralytics.data.converter import yolo_bbox2segment
yolo_bbox2segment(
im_dir="path/to/images",
save_dir=None, # saved to "labels-segment" in the images directory
sam_model="sam_b.pt",
)
```
For more information, visit the [yolo_bbox2segment reference page](../reference/data/converter.md#ultralytics.data.converter.yolo_bbox2segment).
docs/en/yolov5/environments/aws_quickstart_tutorial.md 0 → 100644
View file @ a74dc9a0
---
comments: true
description: Discover how to set up and run YOLOv5 on AWS Deep Learning Instances. Follow our comprehensive guide to get started quickly and cost-effectively.
keywords: YOLOv5, AWS, Deep Learning, Machine Learning, AWS EC2, YOLOv5 setup, Deep Learning Instances, AI, Object Detection
---
# YOLOv5 🚀 on AWS Deep Learning Instance: Your Complete Guide
Setting up a high-performance deep learning environment can be daunting for newcomers, but fear not! 🛠️ With this guide, we'll walk you through the process of getting YOLOv5 up and running on an AWS Deep Learning instance. By leveraging the power of Amazon Web Services (AWS), even those new to [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) can get started quickly and cost-effectively. The AWS platform's scalability is perfect for both experimentation and production deployment.
Other quickstart options for YOLOv5 include our [Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>, [GCP Deep Learning VM](./google_cloud_quickstart_tutorial.md), and our Docker image at [Docker Hub](https://hub.docker.com/r/ultralytics/yolov5) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>.
## Step 1: AWS Console Sign-In
Start by creating an account or signing in to the AWS console at [https://aws.amazon.com/console/](https://aws.amazon.com/console/). Once logged in, select the **EC2** service to manage and set up your instances.
![Console](https://github.com/ultralytics/docs/releases/download/0/aws-console-sign-in.avif)
## Step 2: Launch Your Instance
In the EC2 dashboard, you'll find the **Launch Instance** button which is your gateway to creating a new virtual server.
![Launch](https://github.com/ultralytics/docs/releases/download/0/launch-instance-button.avif)
### Selecting the Right Amazon Machine Image (AMI)
Here's where you choose the operating system and software stack for your instance. Type '[Deep Learning](https://www.ultralytics.com/glossary/deep-learning-dl)' into the search field and select the latest Ubuntu-based Deep Learning AMI, unless your needs dictate otherwise. Amazon's Deep Learning AMIs come pre-installed with popular frameworks and GPU drivers to streamline your setup process.
![Choose AMI](https://github.com/ultralytics/docs/releases/download/0/choose-ami.avif)
### Picking an Instance Type
For deep learning tasks, selecting a GPU instance type is generally recommended as it can vastly accelerate model training. For instance size considerations, remember that the model's memory requirements should never exceed what your instance can provide.
**Note:** The size of your model should be a factor in selecting an instance. If your model exceeds an instance's available RAM, select a different instance type with enough memory for your application.
For a list of available GPU instance types, visit [EC2 Instance Types](https://aws.amazon.com/ec2/instance-types/), specifically under Accelerated Computing.
![Choose Type](https://github.com/ultralytics/docs/releases/download/0/choose-instance-type.avif)
For more information on GPU monitoring and optimization, see [GPU Monitoring and Optimization](https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-gpu.html). For pricing, see [On-Demand Pricing](https://aws.amazon.com/ec2/pricing/on-demand/) and [Spot Pricing](https://aws.amazon.com/ec2/spot/pricing/).
### Configuring Your Instance
Amazon EC2 Spot Instances offer a cost-effective way to run applications as they allow you to bid for unused capacity at a fraction of the standard cost. For a persistent experience that retains data even when the Spot Instance goes down, opt for a persistent request.
![Spot Request](https://github.com/ultralytics/docs/releases/download/0/spot-request.avif)
Remember to adjust the rest of your instance settings and security configurations as needed in Steps 4-7 before launching.
## Step 3: Connect to Your Instance
Once your instance is running, select its checkbox and click Connect to access the SSH information. Use the displayed SSH command in your preferred terminal to establish a connection to your instance.
![Connect](https://github.com/ultralytics/docs/releases/download/0/connect-to-instance.avif)
## Step 4: Running YOLOv5
Logged into your instance, you're now ready to clone the YOLOv5 repository and install dependencies within a Python 3.8 or later environment. YOLOv5's models and datasets will automatically download from the latest [release](https://github.com/ultralytics/yolov5/releases).
```bash
git clone https://github.com/ultralytics/yolov5 # clone repository
cd yolov5
pip install -r requirements.txt # install dependencies
```
With your environment set up, you can begin training, validating, performing inference, and exporting your YOLOv5 models:
```bash
# Train a model on your data
python train.py
# Validate the trained model for Precision, Recall, and mAP
python val.py --weights yolov5s.pt
# Run inference using the trained model on your images or videos
python detect.py --weights yolov5s.pt --source path/to/images
# Export the trained model to other formats for deployment
python export.py --weights yolov5s.pt --include onnx coreml tflite
```
## Optional Extras
To add more swap memory, which can be a savior for large datasets, run:
```bash
sudo fallocate -l 64G /swapfile # allocate 64GB swap file
sudo chmod 600 /swapfile # modify permissions
sudo mkswap /swapfile # set up a Linux swap area
sudo swapon /swapfile # activate swap file
free -h # verify swap memory
```
And that's it! 🎉 You've successfully created an AWS Deep Learning instance and run YOLOv5. Whether you're just starting with [object detection](https://www.ultralytics.com/glossary/object-detection) or scaling up for production, this setup can help you achieve your machine learning goals. Happy training, validating, and deploying! If you encounter any hiccups along the way, the robust AWS documentation and the active Ultralytics community are here to support you.
docs/en/yolov5/environments/azureml_quickstart_tutorial.md 0 → 100644
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---
comments: true
description: Learn how to set up and run YOLOv5 on AzureML. Follow this quickstart guide for easy configuration and model training on an AzureML compute instance.
keywords: YOLOv5, AzureML, machine learning, compute instance, quickstart, model training, virtual environment, Python, AI, deep learning
---
# YOLOv5 🚀 on AzureML
This guide provides a quickstart to use YOLOv5 from an AzureML compute instance.
Note that this guide is a quickstart for quick trials. If you want to unlock the full power AzureML, you can find the documentation to:
- [Create a data asset](https://learn.microsoft.com/azure/machine-learning/how-to-create-data-assets)
- [Create an AzureML job](https://learn.microsoft.com/azure/machine-learning/how-to-train-model)
- [Register a model](https://learn.microsoft.com/azure/machine-learning/how-to-manage-models)
## Prerequisites
You need an [AzureML workspace](https://learn.microsoft.com/azure/machine-learning/concept-workspace?view=azureml-api-2).
## Create a compute instance
From your AzureML workspace, select Compute > Compute instances > New, select the instance with the resources you need.
<img width="1741" alt="create-compute-arrow" src="https://github.com/ultralytics/docs/releases/download/0/create-compute-arrow.avif">
## Open a Terminal
Now from the Notebooks view, open a Terminal and select your compute.
![open-terminal-arrow](https://github.com/ultralytics/docs/releases/download/0/open-terminal-arrow.avif)
## Setup and run YOLOv5
Now you can, create a virtual environment:
```bash
conda create --name yolov5env -y
conda activate yolov5env
conda install pip -y
```
Clone YOLOv5 repository with its submodules:
```bash
git clone https://github.com/ultralytics/yolov5
cd yolov5
git submodule update --init --recursive # Note that you might have a message asking you to add your folder as a safe.directory just copy the recommended command
```
Install the required dependencies:
```bash
pip install -r yolov5/requirements.txt
pip install onnx>=1.10.0
```
Train the YOLOv5 model:
```bash
python train.py
```
Validate the model for [Precision](https://www.ultralytics.com/glossary/precision), [Recall](https://www.ultralytics.com/glossary/recall), and mAP
```bash
python val.py --weights yolov5s.pt
```
Run inference on images and videos:
```bash
python detect.py --weights yolov5s.pt --source path/to/images
```
Export models to other formats:
```bash
python detect.py --weights yolov5s.pt --source path/to/images
```
## Notes on using a notebook
Note that if you want to run these commands from a Notebook, you need to [create a new Kernel](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-terminal?view=azureml-api-2#add-new-kernels) and select your new Kernel on the top of your Notebook.
If you create Python cells it will automatically use your custom environment, but if you add bash cells, you will need to run `source activate <your-env>` on each of these cells to make sure it uses your custom environment.
For example:
```bash
%%bash
source activate newenv
python val.py --weights yolov5s.pt
```
docs/en/yolov5/environments/docker_image_quickstart_tutorial.md 0 → 100644
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---
comments: true
description: Learn how to set up and run YOLOv5 in a Docker container with step-by-step instructions. Explore other quickstart options for an easy setup.
keywords: YOLOv5, Docker, Ultralytics, setup, guide, tutorial, machine learning, deep learning, AI, GPU, NVIDIA, container
---
# Get Started with YOLOv5 🚀 in Docker
This tutorial will guide you through the process of setting up and running YOLOv5 in a Docker container.
You can also explore other quickstart options for YOLOv5, such as our [Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>, [GCP Deep Learning VM](./google_cloud_quickstart_tutorial.md), and [Amazon AWS](./aws_quickstart_tutorial.md).
## Prerequisites
1. **NVIDIA Driver**: Version 455.23 or higher. Download from [NVIDIA's website](https://www.nvidia.com/Download/index.aspx).
2. **NVIDIA-Docker**: Allows Docker to interact with your local GPU. Installation instructions are available on the [NVIDIA-Docker GitHub repository](https://github.com/NVIDIA/nvidia-docker).
3. **Docker Engine - CE**: Version 19.03 or higher. Download and installation instructions can be found on the [Docker website](https://docs.docker.com/get-started/get-docker/).
## Step 1: Pull the YOLOv5 Docker Image
The Ultralytics YOLOv5 DockerHub repository is available at [https://hub.docker.com/r/ultralytics/yolov5](https://hub.docker.com/r/ultralytics/yolov5). Docker Autobuild ensures that the `ultralytics/yolov5:latest` image is always in sync with the most recent repository commit. To pull the latest image, run the following command:
```bash
sudo docker pull ultralytics/yolov5:latest
```
## Step 2: Run the Docker Container
### Basic container:
Run an interactive instance of the YOLOv5 Docker image (called a "container") using the `-it` flag:
```bash
sudo docker run --ipc=host -it ultralytics/yolov5:latest
```
### Container with local file access:
To run a container with access to local files (e.g., COCO [training data](https://www.ultralytics.com/glossary/training-data) in `/datasets`), use the `-v` flag:
```bash
sudo docker run --ipc=host -it -v "$(pwd)"/datasets:/usr/src/datasets ultralytics/yolov5:latest
```
### Container with GPU access:
To run a container with GPU access, use the `--gpus all` flag:
```bash
sudo docker run --ipc=host -it --gpus all ultralytics/yolov5:latest
```
## Step 3: Use YOLOv5 🚀 within the Docker Container
Now you can train, test, detect, and export YOLOv5 models within the running Docker container:
```bash
# Train a model on your data
python train.py
# Validate the trained model for Precision, Recall, and mAP
python val.py --weights yolov5s.pt
# Run inference using the trained model on your images or videos
python detect.py --weights yolov5s.pt --source path/to/images
# Export the trained model to other formats for deployment
python export.py --weights yolov5s.pt --include onnx coreml tflite
```
<p align="center"><img width="1000" src="https://github.com/ultralytics/docs/releases/download/0/gcp-running-docker.avif" alt="GCP running Docker"></p>
docs/en/yolov5/environments/google_cloud_quickstart_tutorial.md 0 → 100644
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---
comments: true
description: Master YOLOv5 deployment on Google Cloud Platform Deep Learning VM. Perfect for AI beginners and experts to achieve high-performance object detection.
keywords: YOLOv5, Google Cloud Platform, GCP, Deep Learning VM, object detection, AI, machine learning, tutorial
---
# Mastering YOLOv5 🚀 Deployment on Google Cloud Platform (GCP) Deep Learning Virtual Machine (VM) ⭐
Embarking on the journey of [artificial intelligence](https://www.ultralytics.com/glossary/artificial-intelligence-ai) and machine learning can be exhilarating, especially when you leverage the power and flexibility of a cloud platform. Google Cloud Platform (GCP) offers robust tools tailored for machine learning enthusiasts and professionals alike. One such tool is the Deep Learning VM that is preconfigured for data science and ML tasks. In this tutorial, we will navigate through the process of setting up YOLOv5 on a GCP Deep Learning VM. Whether you're taking your first steps in ML or you're a seasoned practitioner, this guide is designed to provide you with a clear pathway to implementing object detection models powered by YOLOv5.
🆓 Plus, if you're a fresh GCP user, you're in luck with a [$300 free credit offer](https://cloud.google.com/free/docs/free-cloud-features#free-trial) to kickstart your projects.
In addition to GCP, explore other accessible quickstart options for YOLOv5, like our [Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"> for a browser-based experience, or the scalability of [Amazon AWS](./aws_quickstart_tutorial.md). Furthermore, container aficionados can utilize our official Docker image at [Docker Hub](https://hub.docker.com/r/ultralytics/yolov5) <img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"> for an encapsulated environment.
## Step 1: Create and Configure Your [Deep Learning](https://www.ultralytics.com/glossary/deep-learning-dl) VM
Let's begin by creating a virtual machine that's tuned for deep learning:
1. Head over to the [GCP marketplace](https://console.cloud.google.com/marketplace/details/click-to-deploy-images/deeplearning) and select the **Deep Learning VM**.
2. Opt for a **n1-standard-8** instance; it offers a balance of 8 vCPUs and 30 GB of memory, ideally suited for our needs.
3. Next, select a GPU. This depends on your workload; even a basic one like the T4 will markedly accelerate your model training.
4. Tick the box for 'Install NVIDIA GPU driver automatically on first startup?' for hassle-free setup.
5. Allocate a 300 GB SSD Persistent Disk to ensure you don't bottleneck on I/O operations.
6. Hit 'Deploy' and let GCP do its magic in provisioning your custom Deep Learning VM.
This VM comes loaded with a treasure trove of preinstalled tools and frameworks, including the [Anaconda](https://www.anaconda.com/) Python distribution, which conveniently bundles all the necessary dependencies for YOLOv5.
![GCP Marketplace illustration of setting up a Deep Learning VM](https://github.com/ultralytics/docs/releases/download/0/gcp-deep-learning-vm-setup.avif)
## Step 2: Ready the VM for YOLOv5
Following the environment setup, let's get YOLOv5 up and running:
```bash
# Clone the YOLOv5 repository
git clone https://github.com/ultralytics/yolov5
# Change the directory to the cloned repository
cd yolov5
# Install the necessary Python packages from requirements.txt
pip install -r requirements.txt
```
This setup process ensures you're working with a Python environment version 3.8.0 or newer and [PyTorch](https://www.ultralytics.com/glossary/pytorch) 1.8 or above. Our scripts smoothly download [models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) rending from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases), making it hassle-free to start model training.
## Step 3: Train and Deploy Your YOLOv5 Models 🌐
With the setup complete, you're ready to delve into training and inference with YOLOv5 on your GCP VM:
```bash
# Train a model on your data
python train.py
# Validate the trained model for Precision, Recall, and mAP
python val.py --weights yolov5s.pt
# Run inference using the trained model on your images or videos
python detect.py --weights yolov5s.pt --source path/to/images
# Export the trained model to other formats for deployment
python export.py --weights yolov5s.pt --include onnx coreml tflite
```
With just a few commands, YOLOv5 allows you to train custom [object detection](https://www.ultralytics.com/glossary/object-detection) models tailored to your specific needs or utilize pre-trained weights for quick results on a variety of tasks.
![Terminal command image illustrating model training on a GCP Deep Learning VM](https://github.com/ultralytics/docs/releases/download/0/terminal-command-model-training.avif)
## Allocate Swap Space (optional)
For those dealing with hefty datasets, consider amplifying your GCP instance with an additional 64GB of swap memory:
```bash
sudo fallocate -l 64G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile
free -h # confirm the memory increment
```
### Concluding Thoughts
Congratulations! You are now empowered to harness the capabilities of YOLOv5 with the computational prowess of Google Cloud Platform. This combination provides scalability, efficiency, and versatility for your object detection tasks. Whether for personal projects, academic research, or industrial applications, you have taken a pivotal step into the world of AI and [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) on the cloud.
Do remember to document your journey, share insights with the Ultralytics community, and leverage the collaborative arenas such as [GitHub discussions](https://github.com/ultralytics/yolov5/discussions) to grow further. Now, go forth and innovate with YOLOv5 and GCP! 🌟
Want to keep improving your ML skills and knowledge? Dive into our [documentation and tutorials](../../index.md) for more resources. Let your AI adventure continue!
docs/en/yolov5/index.md 0 → 100644
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---
comments: true
description: Explore comprehensive YOLOv5 documentation with step-by-step tutorials on training, deployment, and model optimization. Empower your vision projects today!.
keywords: YOLOv5, Ultralytics, object detection, computer vision, deep learning, AI, tutorials, PyTorch, model optimization, machine learning, neural networks
---
# Comprehensive Guide to Ultralytics YOLOv5
<div align="center">
<p>
<a href="https://www.ultralytics.com/yolo" target="_blank">
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov5-splash.avif" alt="Ultralytics YOLOv5 v7.0 banner"></a>
</p>
<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
<br>
<a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
<a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
<br>
<br>
Welcome to the Ultralytics' <a href="https://github.com/ultralytics/yolov5">YOLOv5</a>🚀 Documentation! YOLOv5, the fifth iteration of the revolutionary "You Only Look Once" <a href="https://www.ultralytics.com/glossary/object-detection">object detection</a> model, is designed to deliver high-speed, high-accuracy results in real-time.
<br><br>
Built on PyTorch, this powerful <a href="https://www.ultralytics.com/glossary/deep-learning-dl">deep learning</a> framework has garnered immense popularity for its versatility, ease of use, and high performance. Our documentation guides you through the installation process, explains the architectural nuances of the model, showcases various use-cases, and provides a series of detailed tutorials. These resources will help you harness the full potential of YOLOv5 for your <a href="https://www.ultralytics.com/glossary/computer-vision-cv">computer vision</a> projects. Let's get started!
</div>
## Explore and Learn
Here's a compilation of comprehensive tutorials that will guide you through different aspects of YOLOv5.
- [Train Custom Data](tutorials/train_custom_data.md) 🚀 RECOMMENDED: Learn how to train the YOLOv5 model on your custom dataset.
- [Tips for Best Training Results](tutorials/tips_for_best_training_results.md) ☘️: Uncover practical tips to optimize your model training process.
- [Multi-GPU Training](tutorials/multi_gpu_training.md): Understand how to leverage multiple GPUs to expedite your training.
- [PyTorch Hub](tutorials/pytorch_hub_model_loading.md) 🌟 NEW: Learn to load pre-trained models via PyTorch Hub.
- [TFLite, ONNX, CoreML, TensorRT Export](tutorials/model_export.md) 🚀: Understand how to export your model to different formats.
- [Test-Time Augmentation (TTA)](tutorials/test_time_augmentation.md): Explore how to use TTA to improve your model's prediction accuracy.
- [Model Ensembling](tutorials/model_ensembling.md): Learn the strategy of combining multiple models for improved performance.
- [Model Pruning/Sparsity](tutorials/model_pruning_and_sparsity.md): Understand pruning and sparsity concepts, and how to create a more efficient model.
- [Hyperparameter Evolution](tutorials/hyperparameter_evolution.md): Discover the process of automated [hyperparameter tuning](https://www.ultralytics.com/glossary/hyperparameter-tuning) for better model performance.
- [Transfer Learning with Frozen Layers](tutorials/transfer_learning_with_frozen_layers.md): Learn how to implement [transfer learning](https://www.ultralytics.com/glossary/transfer-learning) by freezing layers in YOLOv5.
- [Architecture Summary](tutorials/architecture_description.md) 🌟 Delve into the structural details of the YOLOv5 model.
- [Roboflow for Datasets](tutorials/roboflow_datasets_integration.md): Understand how to utilize Roboflow for dataset management, labeling, and [active learning](https://www.ultralytics.com/glossary/active-learning).
- [ClearML Logging](tutorials/clearml_logging_integration.md) 🌟 Learn how to integrate ClearML for efficient logging during your model training.
- [YOLOv5 with Neural Magic](tutorials/neural_magic_pruning_quantization.md) Discover how to use Neural Magic's Deepsparse to prune and quantize your YOLOv5 model.
- [Comet Logging](tutorials/comet_logging_integration.md) 🌟 NEW: Explore how to utilize Comet for improved model training logging.
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
- **Free GPU Notebooks**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](environments/azureml_quickstart_tutorial.md)
- **Docker**: [Docker Quickstart Guide](environments/docker_image_quickstart_tutorial.md) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
## Project Status
<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
This badge indicates that all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are successfully passing. These CI tests rigorously check the functionality and performance of YOLOv5 across various key aspects: [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py), and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py). They ensure consistent and reliable operation on macOS, Windows, and Ubuntu, with tests conducted every 24 hours and upon each new commit.
<br>
<div align="center">
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
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</div>
## Connect and Contribute
Your journey with YOLOv5 doesn't have to be a solitary one. Join our vibrant community on [GitHub](https://github.com/ultralytics/yolov5), connect with professionals on [LinkedIn](https://www.linkedin.com/company/ultralytics/), share your results on [Twitter](https://twitter.com/ultralytics), and find educational resources on [YouTube](https://www.youtube.com/ultralytics?sub_confirmation=1). Follow us on [TikTok](https://www.tiktok.com/@ultralytics) and [BiliBili](https://ultralytics.com/bilibili) for more engaging content.
Interested in contributing? We welcome contributions of all forms; from code improvements and bug reports to documentation updates. Check out our [contributing guidelines](../help/contributing.md/) for more information.
We're excited to see the innovative ways you'll use YOLOv5. Dive in, experiment, and revolutionize your computer vision projects! 🚀
## FAQ
### What are the key features of Ultralytics YOLOv5?
Ultralytics YOLOv5 is renowned for its high-speed and high-[accuracy](https://www.ultralytics.com/glossary/accuracy) object detection capabilities. Built on [PyTorch](https://www.ultralytics.com/glossary/pytorch), it is versatile and user-friendly, making it suitable for various computer vision projects. Key features include real-time inference, support for multiple training tricks like Test-Time Augmentation (TTA) and Model Ensembling, and compatibility with export formats such as TFLite, ONNX, CoreML, and TensorRT. To delve deeper into how Ultralytics YOLOv5 can elevate your project, explore our [TFLite, ONNX, CoreML, TensorRT Export guide](tutorials/model_export.md).
### How can I train a custom YOLOv5 model on my dataset?
Training a custom YOLOv5 model on your dataset involves a few key steps. First, prepare your dataset in the required format, annotated with labels. Then, configure the YOLOv5 training parameters and start the training process using the `train.py` script. For an in-depth tutorial on this process, consult our [Train Custom Data guide](tutorials/train_custom_data.md). It provides step-by-step instructions to ensure optimal results for your specific use case.
### Why should I use Ultralytics YOLOv5 over other object detection models like RCNN?
Ultralytics YOLOv5 is preferred over models like RCNN due to its superior speed and accuracy in real-time object detection. YOLOv5 processes the entire image in one go, making it significantly faster compared to the region-based approach of RCNN, which involves multiple passes. Additionally, YOLOv5's seamless integration with various export formats and extensive documentation make it an excellent choice for both beginners and professionals. Learn more about the architectural advantages in our [Architecture Summary](tutorials/architecture_description.md).
### How can I optimize YOLOv5 model performance during training?
Optimizing YOLOv5 model performance involves tuning various hyperparameters and incorporating techniques like [data augmentation](https://www.ultralytics.com/glossary/data-augmentation) and transfer learning. Ultralytics provides comprehensive resources on hyperparameter evolution and pruning/sparsity to improve model efficiency. You can discover practical tips in our [Tips for Best Training Results guide](tutorials/tips_for_best_training_results.md), which offers actionable insights for achieving optimal performance during training.
### What environments are supported for running YOLOv5 applications?
Ultralytics YOLOv5 supports a variety of environments, including free GPU notebooks on Gradient, Google Colab, Kaggle, as well as major cloud platforms like Google Cloud, Amazon AWS, and Azure. Docker images are also available for convenient setup. For a detailed guide on setting up these environments, check our [Supported Environments](tutorials/roboflow_datasets_integration.md) section, which includes step-by-step instructions for each platform.
docs/en/yolov5/quickstart_tutorial.md 0 → 100644
View file @ a74dc9a0
---
comments: true
description: Kickstart your real-time object detection journey with YOLOv5! This guide covers installation, inference, and training to help you master YOLOv5 quickly.
keywords: YOLOv5, Quickstart, real-time object detection, AI, ML, PyTorch, inference, training, Ultralytics, machine learning, deep learning
---
# YOLOv5 Quickstart 🚀
Embark on your journey into the dynamic realm of real-time [object detection](https://www.ultralytics.com/glossary/object-detection) with YOLOv5! This guide is crafted to serve as a comprehensive starting point for AI enthusiasts and professionals aiming to master YOLOv5. From initial setup to advanced training techniques, we've got you covered. By the end of this guide, you'll have the knowledge to implement YOLOv5 into your projects confidently. Let's ignite the engines and soar into YOLOv5!
## Install
Prepare for launch by cloning the repository and establishing the environment. This ensures that all the necessary [requirements](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) are installed. Check that you have [**Python>=3.8.0**](https://www.python.org/) and [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) ready for takeoff.
```bash
git clone https://github.com/ultralytics/yolov5 # clone repository
cd yolov5
pip install -r requirements.txt # install dependencies
```
## Inference with [PyTorch](https://www.ultralytics.com/glossary/pytorch) Hub
Experience the simplicity of YOLOv5 [PyTorch Hub](./tutorials/pytorch_hub_model_loading.md) inference, where [models](https://github.com/ultralytics/yolov5/tree/master/models) are seamlessly downloaded from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
```python
import torch
# Model loading
model = torch.hub.load("ultralytics/yolov5", "yolov5s") # Can be 'yolov5n' - 'yolov5x6', or 'custom'
# Inference on images
img = "https://ultralytics.com/images/zidane.jpg" # Can be a file, Path, PIL, OpenCV, numpy, or list of images
# Run inference
results = model(img)
# Display results
results.print() # Other options: .show(), .save(), .crop(), .pandas(), etc.
```
## Inference with detect.py
Harness `detect.py` for versatile inference on various sources. It automatically fetches [models](https://github.com/ultralytics/yolov5/tree/master/models) from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saves results with ease.
```bash
python detect.py --weights yolov5s.pt --source 0 # webcam
image.jpg # image
video.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
'https://youtu.be/LNwODJXcvt4' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
```
## Training
Replicate the YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) benchmarks with the instructions below. The necessary [models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) are pulled directly from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training YOLOv5n/s/m/l/x on a V100 GPU should typically take 1/2/4/6/8 days respectively (note that [Multi-GPU](./tutorials/multi_gpu_training.md) setups work faster). Maximize performance by using the highest possible `--batch-size` or use `--batch-size -1` for the YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092) feature. The following [batch sizes](https://www.ultralytics.com/glossary/batch-size) are ideal for V100-16GB GPUs.
```bash
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
yolov5s 64
yolov5m 40
yolov5l 24
yolov5x 16
```
<img width="800" src="https://github.com/ultralytics/docs/releases/download/0/yolov5-training-curves.avif" alt="YOLO training curves">
To conclude, YOLOv5 is not only a state-of-the-art tool for object detection but also a testament to the power of [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) in transforming the way we interact with the world through visual understanding. As you progress through this guide and begin applying YOLOv5 to your projects, remember that you are at the forefront of a technological revolution, capable of achieving remarkable feats. Should you need further insights or support from fellow visionaries, you're invited to our [GitHub repository](https://github.com/ultralytics/yolov5) home to a thriving community of developers and researchers. Keep exploring, keep innovating, and enjoy the marvels of YOLOv5. Happy detecting! 🌠🔍
docs/en/yolov5/tutorials/architecture_description.md 0 → 100644
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---
comments: true
description: Dive deep into the powerful YOLOv5 architecture by Ultralytics, exploring its model structure, data augmentation techniques, training strategies, and loss computations.
keywords: YOLOv5 architecture, object detection, Ultralytics, YOLO, model structure, data augmentation, training strategies, loss computations, deep learning, machine learning
---
# Ultralytics YOLOv5 Architecture
YOLOv5 (v6.0/6.1) is a powerful object detection algorithm developed by Ultralytics. This article dives deep into the YOLOv5 architecture, [data augmentation](https://www.ultralytics.com/glossary/data-augmentation) strategies, training methodologies, and loss computation techniques. This comprehensive understanding will help improve your practical application of object detection in various fields, including surveillance, autonomous vehicles, and [image recognition](https://www.ultralytics.com/glossary/image-recognition).
## 1. Model Structure
YOLOv5's architecture consists of three main parts:
- **Backbone**: This is the main body of the network. For YOLOv5, the backbone is designed using the `New CSP-Darknet53` structure, a modification of the Darknet architecture used in previous versions.
- **Neck**: This part connects the backbone and the head. In YOLOv5, `SPPF` and `New CSP-PAN` structures are utilized.
- **Head**: This part is responsible for generating the final output. YOLOv5 uses the `YOLOv3 Head` for this purpose.
The structure of the model is depicted in the image below. The model structure details can be found in `yolov5l.yaml`.
![yolov5](https://github.com/ultralytics/docs/releases/download/0/yolov5-model-structure.avif)
YOLOv5 introduces some minor changes compared to its predecessors:
1. The `Focus` structure, found in earlier versions, is replaced with a `6x6 Conv2d` structure. This change boosts efficiency [#4825](https://github.com/ultralytics/yolov5/issues/4825).
2. The `SPP` structure is replaced with `SPPF`. This alteration more than doubles the speed of processing.
To test the speed of `SPP` and `SPPF`, the following code can be used:
<details>
<summary>SPP vs SPPF speed profiling example (click to open)</summary>
```python
import time
import torch
import torch.nn as nn
class SPP(nn.Module):
def __init__(self):
"""Initializes an SPP module with three different sizes of max pooling layers."""
super().__init__()
self.maxpool1 = nn.MaxPool2d(5, 1, padding=2)
self.maxpool2 = nn.MaxPool2d(9, 1, padding=4)
self.maxpool3 = nn.MaxPool2d(13, 1, padding=6)
def forward(self, x):
"""Applies three max pooling layers on input `x` and concatenates results along channel dimension."""
o1 = self.maxpool1(x)
o2 = self.maxpool2(x)
o3 = self.maxpool3(x)
return torch.cat([x, o1, o2, o3], dim=1)
class SPPF(nn.Module):
def __init__(self):
"""Initializes an SPPF module with a specific configuration of MaxPool2d layer."""
super().__init__()
self.maxpool = nn.MaxPool2d(5, 1, padding=2)
def forward(self, x):
"""Applies sequential max pooling and concatenates results with input tensor."""
o1 = self.maxpool(x)
o2 = self.maxpool(o1)
o3 = self.maxpool(o2)
return torch.cat([x, o1, o2, o3], dim=1)
def main():
"""Compares outputs and performance of SPP and SPPF on a random tensor (8, 32, 16, 16)."""
input_tensor = torch.rand(8, 32, 16, 16)
spp = SPP()
sppf = SPPF()
output1 = spp(input_tensor)
output2 = sppf(input_tensor)
print(torch.equal(output1, output2))
t_start = time.time()
for _ in range(100):
spp(input_tensor)
print(f"SPP time: {time.time() - t_start}")
t_start = time.time()
for _ in range(100):
sppf(input_tensor)
print(f"SPPF time: {time.time() - t_start}")
if __name__ == "__main__":
main()
```
result:
```
True
SPP time: 0.5373051166534424
SPPF time: 0.20780706405639648
```
</details>
## 2. Data Augmentation Techniques
YOLOv5 employs various data augmentation techniques to improve the model's ability to generalize and reduce [overfitting](https://www.ultralytics.com/glossary/overfitting). These techniques include:
- **Mosaic Augmentation**: An image processing technique that combines four training images into one in ways that encourage [object detection](https://www.ultralytics.com/glossary/object-detection) models to better handle various object scales and translations.
![mosaic](https://github.com/ultralytics/docs/releases/download/0/mosaic-augmentation.avif)
- **Copy-Paste Augmentation**: An innovative data augmentation method that copies random patches from an image and pastes them onto another randomly chosen image, effectively generating a new training sample.
![copy-paste](https://github.com/ultralytics/docs/releases/download/0/copy-paste.avif)
- **Random Affine Transformations**: This includes random rotation, scaling, translation, and shearing of the images.
![random-affine](https://github.com/ultralytics/docs/releases/download/0/random-affine-transformations.avif)
- **MixUp Augmentation**: A method that creates composite images by taking a linear combination of two images and their associated labels.
![mixup](https://github.com/ultralytics/docs/releases/download/0/mixup.avif)
- **Albumentations**: A powerful library for image augmenting that supports a wide variety of augmentation techniques.
- **HSV Augmentation**: Random changes to the Hue, Saturation, and Value of the images.
![hsv](https://github.com/ultralytics/docs/releases/download/0/hsv-augmentation.avif)
- **Random Horizontal Flip**: An augmentation method that randomly flips images horizontally.
![horizontal-flip](https://github.com/ultralytics/docs/releases/download/0/random-horizontal-flip.avif)
## 3. Training Strategies
YOLOv5 applies several sophisticated training strategies to enhance the model's performance. They include:
- **Multiscale Training**: The input images are randomly rescaled within a range of 0.5 to 1.5 times their original size during the training process.
- **AutoAnchor**: This strategy optimizes the prior anchor boxes to match the statistical characteristics of the ground truth boxes in your custom data.
- **Warmup and Cosine LR Scheduler**: A method to adjust the [learning rate](https://www.ultralytics.com/glossary/learning-rate) to enhance model performance.
- **Exponential Moving Average (EMA)**: A strategy that uses the average of parameters over past steps to stabilize the training process and reduce generalization error.
- **[Mixed Precision](https://www.ultralytics.com/glossary/mixed-precision) Training**: A method to perform operations in half-[precision](https://www.ultralytics.com/glossary/precision) format, reducing memory usage and enhancing computational speed.
- **Hyperparameter Evolution**: A strategy to automatically tune hyperparameters to achieve optimal performance.
## 4. Additional Features
### 4.1 Compute Losses
The loss in YOLOv5 is computed as a combination of three individual loss components:
- **Classes Loss (BCE Loss)**: Binary Cross-Entropy loss, measures the error for the classification task.
- **Objectness Loss (BCE Loss)**: Another Binary Cross-Entropy loss, calculates the error in detecting whether an object is present in a particular grid cell or not.
- **Location Loss (CIoU Loss)**: Complete IoU loss, measures the error in localizing the object within the grid cell.
The overall [loss function](https://www.ultralytics.com/glossary/loss-function) is depicted by:
![loss](https://latex.codecogs.com/svg.image?Loss=\lambda_1L_{cls}+\lambda_2L_{obj}+\lambda_3L_{loc})
### 4.2 Balance Losses
The objectness losses of the three prediction layers (`P3`, `P4`, `P5`) are weighted differently. The balance weights are `[4.0, 1.0, 0.4]` respectively. This approach ensures that the predictions at different scales contribute appropriately to the total loss.
![obj_loss](https://latex.codecogs.com/svg.image?L_{obj}=4.0\cdot&space;L_{obj}^{small}+1.0\cdot&space;L_{obj}^{medium}+0.4\cdot&space;L_{obj}^{large})
### 4.3 Eliminate Grid Sensitivity
The YOLOv5 architecture makes some important changes to the box prediction strategy compared to earlier versions of YOLO. In YOLOv2 and YOLOv3, the box coordinates were directly predicted using the activation of the last layer.
![b_x](<https://latex.codecogs.com/svg.image?b_x=\sigma(t_x)+c_x>)
![b_y](<https://latex.codecogs.com/svg.image?b_y=\sigma(t_y)+c_y>)
![b_w](https://latex.codecogs.com/svg.image?b_w=p_w\cdot&space;e^{t_w})
![b_h](https://latex.codecogs.com/svg.image?b_h=p_h\cdot&space;e^{t_h})
<img src="https://user-images.githubusercontent.com/31005897/158508027-8bf63c28-8290-467b-8a3e-4ad09235001a.png#pic_center" width=40% alt="YOLOv5 grid computation">
However, in YOLOv5, the formula for predicting the box coordinates has been updated to reduce grid sensitivity and prevent the model from predicting unbounded box dimensions.
The revised formulas for calculating the predicted [bounding box](https://www.ultralytics.com/glossary/bounding-box) are as follows:
![bx](<https://latex.codecogs.com/svg.image?b_x=(2\cdot\sigma(t_x)-0.5)+c_x>)
![by](<https://latex.codecogs.com/svg.image?b_y=(2\cdot\sigma(t_y)-0.5)+c_y>)
![bw](<https://latex.codecogs.com/svg.image?b_w=p_w\cdot(2\cdot\sigma(t_w))^2>)
![bh](<https://latex.codecogs.com/svg.image?b_h=p_h\cdot(2\cdot\sigma(t_h))^2>)
Compare the center point offset before and after scaling. The center point offset range is adjusted from (0, 1) to (-0.5, 1.5). Therefore, offset can easily get 0 or 1.
<img src="https://user-images.githubusercontent.com/31005897/158508052-c24bc5e8-05c1-4154-ac97-2e1ec71f582e.png#pic_center" width=40% alt="YOLOv5 grid scaling">
Compare the height and width scaling ratio(relative to anchor) before and after adjustment. The original yolo/darknet box equations have a serious flaw. Width and Height are completely unbounded as they are simply out=exp(in), which is dangerous, as it can lead to runaway gradients, instabilities, NaN losses and ultimately a complete loss of training. [refer this issue](https://github.com/ultralytics/yolov5/issues/471#issuecomment-662009779)
<img src="https://user-images.githubusercontent.com/31005897/158508089-5ac0c7a3-6358-44b7-863e-a6e45babb842.png#pic_center" width=40% alt="YOLOv5 unbounded scaling">
### 4.4 Build Targets
The build target process in YOLOv5 is critical for training efficiency and model [accuracy](https://www.ultralytics.com/glossary/accuracy). It involves assigning ground truth boxes to the appropriate grid cells in the output map and matching them with the appropriate anchor boxes.
This process follows these steps:
- Calculate the ratio of the ground truth box dimensions and the dimensions of each anchor template.
![rw](https://latex.codecogs.com/svg.image?r_w=w_{gt}/w_{at})
![rh](https://latex.codecogs.com/svg.image?r_h=h_{gt}/h_{at})
![rwmax](<https://latex.codecogs.com/svg.image?r_w^{max}=max(r_w,1/r_w)>)
![rhmax](<https://latex.codecogs.com/svg.image?r_h^{max}=max(r_h,1/r_h)>)
![rmax](<https://latex.codecogs.com/svg.image?r^{max}=max(r_w^{max},r_h^{max})>)
![match](https://latex.codecogs.com/svg.image?r^{max}<{\rm&space;anchor_t})
<img src="https://user-images.githubusercontent.com/31005897/158508119-fbb2e483-7b8c-4975-8e1f-f510d367f8ff.png#pic_center" width=70% alt="YOLOv5 IoU computation">
- If the calculated ratio is within the threshold, match the ground truth box with the corresponding anchor.
<img src="https://user-images.githubusercontent.com/31005897/158508771-b6e7cab4-8de6-47f9-9abf-cdf14c275dfe.png#pic_center" width=70% alt="YOLOv5 grid overlap">
- Assign the matched anchor to the appropriate cells, keeping in mind that due to the revised center point offset, a ground truth box can be assigned to more than one anchor. Because the center point offset range is adjusted from (0, 1) to (-0.5, 1.5). GT Box can be assigned to more anchors.
<img src="https://user-images.githubusercontent.com/31005897/158508139-9db4e8c2-cf96-47e0-bc80-35d11512f296.png#pic_center" width=70% alt="YOLOv5 anchor selection">
This way, the build targets process ensures that each ground truth object is properly assigned and matched during the training process, allowing YOLOv5 to learn the task of object detection more effectively.
## Conclusion
In conclusion, YOLOv5 represents a significant step forward in the development of real-time object detection models. By incorporating various new features, enhancements, and training strategies, it surpasses previous versions of the YOLO family in performance and efficiency.
The primary enhancements in YOLOv5 include the use of a dynamic architecture, an extensive range of data augmentation techniques, innovative training strategies, as well as important adjustments in computing losses and the process of building targets. All these innovations significantly improve the accuracy and efficiency of object detection while retaining a high degree of speed, which is the trademark of YOLO models.
docs/en/yolov5/tutorials/clearml_logging_integration.md 0 → 100644
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---
comments: true
description: Learn how to use ClearML for tracking YOLOv5 experiments, data versioning, hyperparameter optimization, and remote execution with ease.
keywords: ClearML, YOLOv5, machine learning, experiment tracking, data versioning, hyperparameter optimization, remote execution, ML pipeline
---
# ClearML Integration
<img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_dark.png#gh-light-mode-only" alt="Clear|ML"><img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_light.png#gh-dark-mode-only" alt="Clear|ML">
## About ClearML
[ClearML](https://clear.ml/) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️.
🔨 Track every YOLOv5 training run in the <b>experiment manager</b>
🔧 Version and easily access your custom [training data](https://www.ultralytics.com/glossary/training-data) with the integrated ClearML <b>Data Versioning Tool</b>
🔦 <b>Remotely train and monitor</b> your YOLOv5 training runs using ClearML Agent
🔬 Get the very best mAP using ClearML <b>Hyperparameter Optimization</b>
🔭 Turn your newly trained <b>YOLOv5 model into an API</b> with just a few commands using ClearML Serving
<br>
And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline!
<br>
<br>
![ClearML scalars dashboard](https://github.com/ultralytics/docs/releases/download/0/clearml-scalars-dashboard.avif)
<br>
<br>
## 🦾 Setting Things Up
To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one:
Either sign up for free to the [ClearML Hosted Service](https://clear.ml/) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go!
- Install the `clearml` python package:
```bash
pip install clearml
```
- Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
```bash
clearml-init
```
That's it! You're done 😎
<br>
## 🚀 Training YOLOv5 With ClearML
To enable ClearML experiment tracking, simply install the ClearML pip package.
```bash
pip install clearml>=1.2.0
```
This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager.
If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py` script, by default the project will be called `YOLOv5` and the task `Training`. PLEASE NOTE: ClearML uses `/` as a delimiter for subprojects, so be careful when using `/` in your project name!
```bash
python train.py --img 640 --batch 16 --epochs 3 --data coco8.yaml --weights yolov5s.pt --cache
```
or with custom project and task name:
```bash
python train.py --project my_project --name my_training --img 640 --batch 16 --epochs 3 --data coco8.yaml --weights yolov5s.pt --cache
```
This will capture:
- Source code + uncommitted changes
- Installed packages
- (Hyper)parameters
- Model files (use `--save-period n` to save a checkpoint every n epochs)
- Console output
- Scalars (mAP_0.5, mAP_0.5:0.95, precision, recall, losses, learning rates, ...)
- General info such as machine details, runtime, creation date etc.
- All produced plots such as label correlogram and [confusion matrix](https://www.ultralytics.com/glossary/confusion-matrix)
- Images with bounding boxes per [epoch](https://www.ultralytics.com/glossary/epoch)
- Mosaic per epoch
- Validation images per epoch
That's a lot right? 🤯 Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them!
There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works!
### 🔗 Dataset Version Management
Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment!
![ClearML Dataset Interface](https://github.com/ultralytics/docs/releases/download/0/clearml-dataset-interface.avif)
### Prepare Your Dataset
The YOLOv5 repository supports a number of different datasets by using YAML files containing their information. By default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the YAML or with the scripts provided by yolov5, you get this folder structure:
```
..
|_ yolov5
|_ datasets
|_ coco128
|_ images
|_ labels
|_ LICENSE
|_ README.txt
```
But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure.
Next, ⚠️**copy the corresponding YAML file to the root of the dataset folder**⚠️. This YAML files contains the information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the structure of the example YAMLs.
Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `names`.
```
..
|_ yolov5
|_ datasets
|_ coco128
|_ images
|_ labels
|_ coco128.yaml # <---- HERE!
|_ LICENSE
|_ README.txt
```
### Upload Your Dataset
To get this dataset into ClearML as a versioned dataset, go to the dataset root folder and run the following command:
```bash
cd coco128
clearml-data sync --project YOLOv5 --name coco128 --folder .
```
The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other:
```bash
# Optionally add --parent <parent_dataset_id> if you want to base
# this version on another dataset version, so no duplicate files are uploaded!
clearml-data create --name coco128 --project YOLOv5
clearml-data add --files .
clearml-data close
```
### Run Training Using A ClearML Dataset
Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models!
```bash
python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_id> --weights yolov5s.pt --cache
```
<br>
### 👀 Hyperparameter Optimization
Now that we have our experiments and data versioned, it's time to take a look at what we can build on top!
Using the code information, installed packages and environment details, the experiment itself is now **completely reproducible**. In fact, ClearML allows you to clone an experiment and even change its parameters. We can then just rerun it with these new parameters automatically, this is basically what HPO does!
To **run hyperparameter optimization locally**, we've included a pre-made script for you. Just make sure a training task has been run at least once, so it is in the ClearML experiment manager, we will essentially clone it and change its hyperparameters.
You'll need to fill in the ID of this `template task` in the script found at `utils/loggers/clearml/hpo.py` and then just run it :) You can change `task.execute_locally()` to `task.execute()` to put it in a ClearML queue and have a remote agent work on it instead.
```bash
# To use optuna, install it first, otherwise you can change the optimizer to just be RandomSearch
pip install optuna
python utils/loggers/clearml/hpo.py
```
![HPO](https://github.com/ultralytics/docs/releases/download/0/hpo-clearml-experiment.avif)
## 🤯 Remote Execution (advanced)
Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs. This is where the ClearML Agent comes into play. Check out what the agent can do here:
- [YouTube video](https://youtu.be/MX3BrXnaULs)
- [Documentation](https://clear.ml/docs/latest/docs/clearml_agent)
In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager.
You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running:
```bash
clearml-agent daemon --queue <queues_to_listen_to> [--docker]
```
### Cloning, Editing And Enqueuing
With our agent running, we can give it some work. Remember from the HPO section that we can clone a task and edit the hyperparameters? We can do that from the interface too!
🪄 Clone the experiment by right-clicking it
🎯 Edit the hyperparameters to what you wish them to be
⏳ Enqueue the task to any of the queues by right-clicking it
![Enqueue a task from the UI](https://github.com/ultralytics/docs/releases/download/0/enqueue-task-ui.avif)
### Executing A Task Remotely
Now you can clone a task like we explained above, or simply mark your current script by adding `task.execute_remotely()` and on execution it will be put into a queue, for the agent to start working on!
To run the YOLOv5 training script remotely, all you have to do is add this line to the training.py script after the clearml logger has been instantiated:
```python
# ...
# Loggers
data_dict = None
if RANK in {-1, 0}:
loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance
if loggers.clearml:
loggers.clearml.task.execute_remotely(queue="my_queue") # <------ ADD THIS LINE
# Data_dict is either None is user did not choose for ClearML dataset or is filled in by ClearML
data_dict = loggers.clearml.data_dict
# ...
```
When running the training script after this change, python will run the script up until that line, after which it will package the code and send it to the queue instead!
### Autoscaling workers
ClearML comes with autoscalers too! This tool will automatically spin up new remote machines in the cloud of your choice (AWS, GCP, Azure) and turn them into ClearML agents for you whenever there are experiments detected in the queue. Once the tasks are processed, the autoscaler will automatically shut down the remote machines, and you stop paying!
Check out the autoscalers getting started video below.
[![Watch the video](https://github.com/ultralytics/docs/releases/download/0/clearml-autoscalers-video-thumbnail.avif)](https://youtu.be/j4XVMAaUt3E)
docs/en/yolov5/tutorials/comet_logging_integration.md 0 → 100644
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---
comments: true
description: Learn to track, visualize and optimize YOLOv5 model metrics with Comet for seamless machine learning workflows.
keywords: YOLOv5, Comet, machine learning, model tracking, hyperparameters, visualization, deep learning, logging, metrics
---
![Comet](https://cdn.comet.ml/img/notebook_logo.png)
# YOLOv5 with Comet
This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet2)
## About Comet
Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models.
Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!
## Getting Started
### Install Comet
```shell
pip install comet_ml
```
### Configure Comet Credentials
There are two ways to configure Comet with YOLOv5.
You can either set your credentials through environment variables
**Environment Variables**
```shell
export COMET_API_KEY=<Your Comet API Key>
export COMET_PROJECT_NAME=<Your Comet Project Name> # This will default to 'yolov5'
```
Or create a `.comet.config` file in your working directory and set your credentials there.
**Comet Configuration File**
```
[comet]
api_key=<Your Comet API Key>
project_name=<Your Comet Project Name> # This will default to 'yolov5'
```
### Run the Training Script
```shell
# Train YOLOv5s on COCO128 for 5 epochs
python train.py --img 640 --batch 16 --epochs 5 --data coco128.yaml --weights yolov5s.pt
```
That's it! Comet will automatically log your hyperparameters, command line arguments, training and validation metrics. You can visualize and analyze your runs in the Comet UI
<img width="1920" alt="yolo-ui" src="https://github.com/ultralytics/docs/releases/download/0/yolo-ui.avif">
## Try out an Example!
Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
Or better yet, try it out yourself in this Colab Notebook
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)
## Log automatically
By default, Comet will log the following items
## Metrics
- Box Loss, Object Loss, Classification Loss for the training and [validation data](https://www.ultralytics.com/glossary/validation-data)
- mAP_0.5, mAP_0.5:0.95 metrics for the validation data.
- Precision and Recall for the validation data
## Parameters
- Model Hyperparameters
- All parameters passed through the command line options
## Visualizations
- [Confusion Matrix](https://www.ultralytics.com/glossary/confusion-matrix) of the model predictions on the validation data
- Plots for the PR and F1 curves across all classes
- Correlogram of the Class Labels
## Configure Comet Logging
Comet can be configured to log additional data either through command line flags passed to the training script or through environment variables.
```shell
export COMET_MODE=online # Set whether to run Comet in 'online' or 'offline' mode. Defaults to online
export COMET_MODEL_NAME=<your model name> #Set the name for the saved model. Defaults to yolov5
export COMET_LOG_CONFUSION_MATRIX=false # Set to disable logging a Comet Confusion Matrix. Defaults to true
export COMET_MAX_IMAGE_UPLOADS=<number of allowed images to upload to Comet> # Controls how many total image predictions to log to Comet. Defaults to 100.
export COMET_LOG_PER_CLASS_METRICS=true # Set to log evaluation metrics for each detected class at the end of training. Defaults to false
export COMET_DEFAULT_CHECKPOINT_FILENAME=<your checkpoint filename> # Set this if you would like to resume training from a different checkpoint. Defaults to 'last.pt'
export COMET_LOG_BATCH_LEVEL_METRICS=true # Set this if you would like to log training metrics at the batch level. Defaults to false.
export COMET_LOG_PREDICTIONS=true # Set this to false to disable logging model predictions
```
## Logging Checkpoints with Comet
Logging Models to Comet is disabled by default. To enable it, pass the `save-period` argument to the training script. This will save the logged checkpoints to Comet based on the interval value provided by `save-period`
```shell
python train.py \
--img 640 \
--batch 16 \
--epochs 5 \
--data coco128.yaml \
--weights yolov5s.pt \
--save-period 1
```
## Logging Model Predictions
By default, model predictions (images, ground truth labels and bounding boxes) will be logged to Comet.
You can control the frequency of logged predictions and the associated images by passing the `bbox_interval` command line argument. Predictions can be visualized using Comet's [Object Detection](https://www.ultralytics.com/glossary/object-detection) Custom Panel. This frequency corresponds to every Nth batch of data per [epoch](https://www.ultralytics.com/glossary/epoch). In the example below, we are logging every 2nd batch of data for each epoch.
**Note:** The YOLOv5 validation dataloader will default to a [batch size](https://www.ultralytics.com/glossary/batch-size) of 32, so you will have to set the logging frequency accordingly.
Here is an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
```shell
python train.py \
--img 640 \
--batch 16 \
--epochs 5 \
--data coco128.yaml \
--weights yolov5s.pt \
--bbox_interval 2
```
### Controlling the number of Prediction Images logged to Comet
When logging predictions from YOLOv5, Comet will log the images associated with each set of predictions. By default a maximum of 100 validation images are logged. You can increase or decrease this number using the `COMET_MAX_IMAGE_UPLOADS` environment variable.
```shell
env COMET_MAX_IMAGE_UPLOADS=200 python train.py \
--img 640 \
--batch 16 \
--epochs 5 \
--data coco128.yaml \
--weights yolov5s.pt \
--bbox_interval 1
```
### Logging Class Level Metrics
Use the `COMET_LOG_PER_CLASS_METRICS` environment variable to log mAP, [precision](https://www.ultralytics.com/glossary/precision), [recall](https://www.ultralytics.com/glossary/recall), f1 for each class.
```shell
env COMET_LOG_PER_CLASS_METRICS=true python train.py \
--img 640 \
--batch 16 \
--epochs 5 \
--data coco128.yaml \
--weights yolov5s.pt
```
## Uploading a Dataset to Comet Artifacts
If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github), you can do so using the `upload_dataset` flag.
The dataset be organized in the way described in the [YOLOv5 documentation](train_custom_data.md). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file.
```shell
python train.py \
--img 640 \
--batch 16 \
--epochs 5 \
--data coco128.yaml \
--weights yolov5s.pt \
--upload_dataset
```
You can find the uploaded dataset in the Artifacts tab in your Comet Workspace <img width="1073" alt="artifact-1" src="https://github.com/ultralytics/docs/releases/download/0/artifact-1.avif">
You can preview the data directly in the Comet UI. <img width="1082" alt="artifact-2" src="https://github.com/ultralytics/docs/releases/download/0/artifact-2.avif">
Artifacts are versioned and also support adding metadata about the dataset. Comet will automatically log the metadata from your dataset `yaml` file <img width="963" alt="artifact-3" src="https://github.com/ultralytics/docs/releases/download/0/artifact-metadata-logging.avif">
### Using a saved Artifact
If you would like to use a dataset from Comet Artifacts, set the `path` variable in your dataset `yaml` file to point to the following Artifact resource URL.
```
# contents of artifact.yaml file
path: "comet://<workspace name>/<artifact name>:<artifact version or alias>"
```
Then pass this file to your training script in the following way
```shell
python train.py \
--img 640 \
--batch 16 \
--epochs 5 \
--data artifact.yaml \
--weights yolov5s.pt
```
Artifacts also allow you to track the lineage of data as it flows through your Experimentation workflow. Here you can see a graph that shows you all the experiments that have used your uploaded dataset. <img width="1391" alt="artifact-4" src="https://github.com/ultralytics/docs/releases/download/0/artifact-lineage-graph.avif">
## Resuming a Training Run
If your training run is interrupted for any reason, e.g. disrupted internet connection, you can resume the run using the `resume` flag and the Comet Run Path.
The Run Path has the following format `comet://<your workspace name>/<your project name>/<experiment id>`.
This will restore the run to its state before the interruption, which includes restoring the model from a checkpoint, restoring all hyperparameters and training arguments and downloading Comet dataset Artifacts if they were used in the original run. The resumed run will continue logging to the existing Experiment in the Comet UI
```shell
python train.py \
--resume "comet://<your run path>"
```
## Hyperparameter Search with the Comet Optimizer
YOLOv5 is also integrated with Comet's Optimizer, making is simple to visualize hyperparameter sweeps in the Comet UI.
### Configuring an Optimizer Sweep
To configure the Comet Optimizer, you will have to create a JSON file with the information about the sweep. An example file has been provided in `utils/loggers/comet/optimizer_config.json`
```shell
python utils/loggers/comet/hpo.py \
--comet_optimizer_config "utils/loggers/comet/optimizer_config.json"
```
The `hpo.py` script accepts the same arguments as `train.py`. If you wish to pass additional arguments to your sweep simply add them after the script.
```shell
python utils/loggers/comet/hpo.py \
--comet_optimizer_config "utils/loggers/comet/optimizer_config.json" \
--save-period 1 \
--bbox_interval 1
```
### Running a Sweep in Parallel
```shell
comet optimizer -j <set number of workers> utils/loggers/comet/hpo.py \
utils/loggers/comet/optimizer_config.json"
```
## Visualizing Results
Comet provides a number of ways to visualize the results of your sweep. Take a look at a [project with a completed sweep here](https://www.comet.com/examples/comet-example-yolov5/view/PrlArHGuuhDTKC1UuBmTtOSXD/panels?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
<img width="1626" alt="hyperparameter-yolo" src="https://github.com/ultralytics/docs/releases/download/0/hyperparameter-yolo.avif">
docs/en/yolov5/tutorials/hyperparameter_evolution.md 0 → 100644
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---
comments: true
description: Learn how to optimize YOLOv5 hyperparameters using genetic algorithms for improved training performance. Step-by-step instructions included.
keywords: YOLOv5, hyperparameter evolution, genetic algorithms, machine learning, optimization, Ultralytics
---
📚 This guide explains **hyperparameter evolution** for YOLOv5 🚀. Hyperparameter evolution is a method of [Hyperparameter Optimization](https://en.wikipedia.org/wiki/Hyperparameter_optimization) using a [Genetic Algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm) (GA) for optimization.
Hyperparameters in ML control various aspects of training, and finding optimal values for them can be a challenge. Traditional methods like grid searches can quickly become intractable due to 1) the high dimensional search space 2) unknown correlations among the dimensions, and 3) expensive nature of evaluating the fitness at each point, making GA a suitable candidate for hyperparameter searches.
## Before You Start
Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
```bash
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
```
## 1. Initialize Hyperparameters
YOLOv5 has about 30 hyperparameters used for various training settings. These are defined in `*.yaml` files in the `/data/hyps` directory. Better initial guesses will produce better final results, so it is important to initialize these values properly before evolving. If in doubt, simply use the default values, which are optimized for YOLOv5 COCO training from scratch.
```yaml
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Hyperparameters for low-augmentation COCO training from scratch
# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.0005 # optimizer weight decay 5e-4
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.1 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.5 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 1.0 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
# anchors: 3 # anchors per output layer (0 to ignore)
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.1 # image translation (+/- fraction)
scale: 0.5 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.0 # image mixup (probability)
copy_paste: 0.0 # segment copy-paste (probability)
```
## 2. Define Fitness
Fitness is the value we seek to maximize. In YOLOv5 we define a default fitness function as a weighted combination of metrics: `mAP@0.5` contributes 10% of the weight and `mAP@0.5:0.95` contributes the remaining 90%, with [Precision `P` and [Recall](https://www.ultralytics.com/glossary/recall) `R`](https://en.wikipedia.org/wiki/Precision_and_recall) absent. You may adjust these as you see fit or use the default fitness definition in utils/metrics.py (recommended).
```python
def fitness(x):
"""Return model fitness as the sum of weighted metrics [P, R, mAP@0.5, mAP@0.5:0.95]."""
w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
return (x[:, :4] * w).sum(1)
```
## 3. Evolve
Evolution is performed about a base scenario which we seek to improve upon. The base scenario in this example is [finetuning](https://www.ultralytics.com/glossary/fine-tuning) COCO128 for 10 [epochs](https://www.ultralytics.com/glossary/epoch) using pretrained YOLOv5s. The base scenario training command is:
```bash
python train.py --epochs 10 --data coco128.yaml --weights yolov5s.pt --cache
```
To evolve hyperparameters **specific to this scenario**, starting from our initial values defined in **Section 1.**, and maximizing the fitness defined in **Section 2.**, append `--evolve`:
```bash
# Single-GPU
python train.py --epochs 10 --data coco128.yaml --weights yolov5s.pt --cache --evolve
# Multi-GPU
for i in 0 1 2 3 4 5 6 7; do
sleep $(expr 30 \* $i) && # 30-second delay (optional)
echo 'Starting GPU '$i'...' &&
nohup python train.py --epochs 10 --data coco128.yaml --weights yolov5s.pt --cache --device $i --evolve > evolve_gpu_$i.log &
done
# Multi-GPU bash-while (not recommended)
for i in 0 1 2 3 4 5 6 7; do
sleep $(expr 30 \* $i) && # 30-second delay (optional)
echo 'Starting GPU '$i'...' &&
"$(while true; do nohup python train.py... --device $i --evolve 1 > evolve_gpu_$i.log; done)" &
done
```
The default evolution settings will run the base scenario 300 times, i.e. for 300 generations. You can modify generations via the `--evolve` argument, i.e. `python train.py --evolve 1000`.
The main genetic operators are **crossover** and **mutation**. In this work mutation is used, with an 80% probability and a 0.04 variance to create new offspring based on a combination of the best parents from all previous generations. Results are logged to `runs/evolve/exp/evolve.csv`, and the highest fitness offspring is saved every generation as `runs/evolve/hyp_evolved.yaml`:
```yaml
# YOLOv5 Hyperparameter Evolution Results
# Best generation: 287
# Last generation: 300
# metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss
# 0.54634, 0.55625, 0.58201, 0.33665, 0.056451, 0.042892, 0.013441
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.0005 # optimizer weight decay 5e-4
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.1 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.5 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 1.0 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
# anchors: 3 # anchors per output layer (0 to ignore)
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.1 # image translation (+/- fraction)
scale: 0.5 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.0 # image mixup (probability)
copy_paste: 0.0 # segment copy-paste (probability)
```
We recommend a minimum of 300 generations of evolution for best results. Note that **evolution is generally expensive and time-consuming**, as the base scenario is trained hundreds of times, possibly requiring hundreds or thousands of GPU hours.
## 4. Visualize
`evolve.csv` is plotted as `evolve.png` by `utils.plots.plot_evolve()` after evolution finishes with one subplot per hyperparameter showing fitness (y-axis) vs hyperparameter values (x-axis). Yellow indicates higher concentrations. Vertical distributions indicate that a parameter has been disabled and does not mutate. This is user selectable in the `meta` dictionary in train.py, and is useful for fixing parameters and preventing them from evolving.
![evolve](https://github.com/ultralytics/docs/releases/download/0/evolve.avif)
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
- **Free GPU Notebooks**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
- **Docker**: [Docker Quickstart Guide](../environments/docker_image_quickstart_tutorial.md) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
## Project Status
<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
This badge indicates that all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are successfully passing. These CI tests rigorously check the functionality and performance of YOLOv5 across various key aspects: [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py), and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py). They ensure consistent and reliable operation on macOS, Windows, and Ubuntu, with tests conducted every 24 hours and upon each new commit.
docs/en/yolov5/tutorials/model_ensembling.md 0 → 100644
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---
comments: true
description: Learn how to use YOLOv5 model ensembling during testing and inference to enhance mAP and Recall for more accurate predictions.
keywords: YOLOv5, model ensembling, testing, inference, mAP, Recall, Ultralytics, object detection, PyTorch
---
📚 This guide explains how to use YOLOv5 🚀 **model ensembling** during testing and inference for improved mAP and [Recall](https://www.ultralytics.com/glossary/recall).
From [https://en.wikipedia.org/wiki/Ensemble_learning](https://en.wikipedia.org/wiki/Ensemble_learning):
> Ensemble modeling is a process where multiple diverse models are created to predict an outcome, either by using many different modeling algorithms or using different [training data](https://www.ultralytics.com/glossary/training-data) sets. The ensemble model then aggregates the prediction of each base model and results in once final prediction for the unseen data. The motivation for using ensemble models is to reduce the generalization error of the prediction. As long as the base models are diverse and independent, the prediction error of the model decreases when the ensemble approach is used. The approach seeks the wisdom of crowds in making a prediction. Even though the ensemble model has multiple base models within the model, it acts and performs as a single model.
## Before You Start
Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
```bash
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
```
## Test Normally
Before ensembling we want to establish the baseline performance of a single model. This command tests YOLOv5x on COCO val2017 at image size 640 pixels. `yolov5x.pt` is the largest and most accurate model available. Other options are `yolov5s.pt`, `yolov5m.pt` and `yolov5l.pt`, or you own checkpoint from training a custom dataset `./weights/best.pt`. For details on all available models please see our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints).
```bash
python val.py --weights yolov5x.pt --data coco.yaml --img 640 --half
```
Output:
```shell
val: data=./data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True
YOLOv5 🚀 v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)
Fusing layers...
Model Summary: 476 layers, 87730285 parameters, 0 gradients
val: Scanning '../datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 2846.03it/s]
val: New cache created: ../datasets/coco/val2017.cache
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [02:30<00:00, 1.05it/s]
all 5000 36335 0.746 0.626 0.68 0.49
Speed: 0.1ms pre-process, 22.4ms inference, 1.4ms NMS per image at shape (32, 3, 640, 640) # <--- baseline speed
Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...
...
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.504 # <--- baseline mAP
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.688
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.546
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.351
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.644
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.382
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.628
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.681 # <--- baseline mAR
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.826
```
## Ensemble Test
Multiple pretrained models may be ensembled together at test and inference time by simply appending extra models to the `--weights` argument in any existing val.py or detect.py command. This example tests an ensemble of 2 models together:
- YOLOv5x
- YOLOv5l6
```bash
python val.py --weights yolov5x.pt yolov5l6.pt --data coco.yaml --img 640 --half
```
Output:
```shell
val: data=./data/coco.yaml, weights=['yolov5x.pt', 'yolov5l6.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, task=val, device=, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True
YOLOv5 🚀 v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)
Fusing layers...
Model Summary: 476 layers, 87730285 parameters, 0 gradients # Model 1
Fusing layers...
Model Summary: 501 layers, 77218620 parameters, 0 gradients # Model 2
Ensemble created with ['yolov5x.pt', 'yolov5l6.pt'] # Ensemble notice
val: Scanning '../datasets/coco/val2017.cache' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:00<00:00, 49695545.02it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [03:58<00:00, 1.52s/it]
all 5000 36335 0.747 0.637 0.692 0.502
Speed: 0.1ms pre-process, 39.5ms inference, 2.0ms NMS per image at shape (32, 3, 640, 640) # <--- ensemble speed
Evaluating pycocotools mAP... saving runs/val/exp3/yolov5x_predictions.json...
...
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.515 # <--- ensemble mAP
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.699
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.557
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.356
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.563
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.668
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.387
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.638
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.689 # <--- ensemble mAR
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.526
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.743
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.844
```
## Ensemble Inference
Append extra models to the `--weights` argument to run ensemble inference:
```bash
python detect.py --weights yolov5x.pt yolov5l6.pt --img 640 --source data/images
```
Output:
```bash
YOLOv5 🚀 v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)
Fusing layers...
Model Summary: 476 layers, 87730285 parameters, 0 gradients
Fusing layers...
Model Summary: 501 layers, 77218620 parameters, 0 gradients
Ensemble created with ['yolov5x.pt', 'yolov5l6.pt']
image 1/2 /content/yolov5/data/images/bus.jpg: 640x512 4 persons, 1 bus, 1 tie, Done. (0.063s)
image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 3 persons, 2 ties, Done. (0.056s)
Results saved to runs/detect/exp2
Done. (0.223s)
```
<img src="https://github.com/ultralytics/docs/releases/download/0/yolo-inference-result.avif" width="500" alt="YOLO inference result">
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
- **Free GPU Notebooks**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
- **Docker**: [Docker Quickstart Guide](../environments/docker_image_quickstart_tutorial.md) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
## Project Status
<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
This badge indicates that all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are successfully passing. These CI tests rigorously check the functionality and performance of YOLOv5 across various key aspects: [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py), and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py). They ensure consistent and reliable operation on macOS, Windows, and Ubuntu, with tests conducted every 24 hours and upon each new commit.
docs/en/yolov5/tutorials/model_export.md 0 → 100644
View file @ a74dc9a0
---
comments: true
description: Learn to export YOLOv5 models to various formats like TFLite, ONNX, CoreML and TensorRT. Increase model efficiency and deployment flexibility with our step-by-step guide.
keywords: YOLOv5 export, TFLite, ONNX, CoreML, TensorRT, model conversion, YOLOv5 tutorial, PyTorch export
---
# TFLite, ONNX, CoreML, TensorRT Export
📚 This guide explains how to export a trained YOLOv5 🚀 model from [PyTorch](https://www.ultralytics.com/glossary/pytorch) to ONNX and TorchScript formats.
## Before You Start
Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
```bash
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
```
For [TensorRT](https://developer.nvidia.com/tensorrt) export example (requires GPU) see our Colab [notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb#scrollTo=VTRwsvA9u7ln&line=2&uniqifier=1) appendix section. <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
## Formats
YOLOv5 inference is officially supported in 11 formats:
💡 ProTip: Export to ONNX or OpenVINO for up to 3x CPU speedup. See [CPU Benchmarks](https://github.com/ultralytics/yolov5/pull/6613). 💡 ProTip: Export to TensorRT for up to 5x GPU speedup. See [GPU Benchmarks](https://github.com/ultralytics/yolov5/pull/6963).
| Format | `export.py --include` | Model |
| :------------------------------------------------------------------------- | :-------------------- | :------------------------ |
| [PyTorch](https://pytorch.org/) | - | `yolov5s.pt` |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov5s.torchscript` |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov5s.onnx` |
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov5s_openvino_model/` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov5s.engine` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov5s.mlmodel` |
| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov5s_saved_model/` |
| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov5s.pb` |
| [TensorFlow Lite](https://ai.google.dev/edge/litert) | `tflite` | `yolov5s.tflite` |
| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov5s_edgetpu.tflite` |
| [TensorFlow.js](https://www.tensorflow.org/js) | `tfjs` | `yolov5s_web_model/` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov5s_paddle_model/` |
## Benchmarks
Benchmarks below run on a Colab Pro with the YOLOv5 tutorial notebook <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>. To reproduce:
```bash
python benchmarks.py --weights yolov5s.pt --imgsz 640 --device 0
```
### Colab Pro V100 GPU
```
benchmarks: weights=/content/yolov5/yolov5s.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=0, half=False, test=False
Checking setup...
YOLOv5 🚀 v6.1-135-g7926afc torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 46.7/166.8 GB disk)
Benchmarks complete (458.07s)
Format mAP@0.5:0.95 Inference time (ms)
0 PyTorch 0.4623 10.19
1 TorchScript 0.4623 6.85
2 ONNX 0.4623 14.63
3 OpenVINO NaN NaN
4 TensorRT 0.4617 1.89
5 CoreML NaN NaN
6 TensorFlow SavedModel 0.4623 21.28
7 TensorFlow GraphDef 0.4623 21.22
8 TensorFlow Lite NaN NaN
9 TensorFlow Edge TPU NaN NaN
10 TensorFlow.js NaN NaN
```
### Colab Pro CPU
```
benchmarks: weights=/content/yolov5/yolov5s.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=cpu, half=False, test=False
Checking setup...
YOLOv5 🚀 v6.1-135-g7926afc torch 1.10.0+cu111 CPU
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 41.5/166.8 GB disk)
Benchmarks complete (241.20s)
Format mAP@0.5:0.95 Inference time (ms)
0 PyTorch 0.4623 127.61
1 TorchScript 0.4623 131.23
2 ONNX 0.4623 69.34
3 OpenVINO 0.4623 66.52
4 TensorRT NaN NaN
5 CoreML NaN NaN
6 TensorFlow SavedModel 0.4623 123.79
7 TensorFlow GraphDef 0.4623 121.57
8 TensorFlow Lite 0.4623 316.61
9 TensorFlow Edge TPU NaN NaN
10 TensorFlow.js NaN NaN
```
## Export a Trained YOLOv5 Model
This command exports a pretrained YOLOv5s model to TorchScript and ONNX formats. `yolov5s.pt` is the 'small' model, the second-smallest model available. Other options are `yolov5n.pt`, `yolov5m.pt`, `yolov5l.pt` and `yolov5x.pt`, along with their P6 counterparts i.e. `yolov5s6.pt` or you own custom training checkpoint i.e. `runs/exp/weights/best.pt`. For details on all available models please see our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints).
```bash
python export.py --weights yolov5s.pt --include torchscript onnx
```
💡 ProTip: Add `--half` to export models at FP16 half [precision](https://www.ultralytics.com/glossary/precision) for smaller file sizes
Output:
```bash
export: data=data/coco128.yaml, weights=['yolov5s.pt'], imgsz=[640, 640], batch_size=1, device=cpu, half=False, inplace=False, train=False, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=12, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['torchscript', 'onnx']
YOLOv5 🚀 v6.2-104-ge3e5122 Python-3.8.0 torch-1.12.1+cu113 CPU
Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt to yolov5s.pt...
100% 14.1M/14.1M [00:00<00:00, 274MB/s]
Fusing layers...
YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients
PyTorch: starting from yolov5s.pt with output shape (1, 25200, 85) (14.1 MB)
TorchScript: starting export with torch 1.12.1+cu113...
TorchScript: export success ✅ 1.7s, saved as yolov5s.torchscript (28.1 MB)
ONNX: starting export with onnx 1.12.0...
ONNX: export success ✅ 2.3s, saved as yolov5s.onnx (28.0 MB)
Export complete (5.5s)
Results saved to /content/yolov5
Detect: python detect.py --weights yolov5s.onnx
Validate: python val.py --weights yolov5s.onnx
PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.onnx')
Visualize: https://netron.app/
```
The 3 exported models will be saved alongside the original PyTorch model:
<p align="center"><img width="700" src="https://github.com/ultralytics/docs/releases/download/0/yolo-export-locations.avif" alt="YOLO export locations"></p>
[Netron Viewer](https://github.com/lutzroeder/netron) is recommended for visualizing exported models:
<p align="center"><img width="850" src="https://github.com/ultralytics/docs/releases/download/0/yolo-model-visualization.avif" alt="YOLO model visualization"></p>
## Exported Model Usage Examples
`detect.py` runs inference on exported models:
```bash
python detect.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with dnn=True
yolov5s_openvino_model # OpenVINO
yolov5s.engine # TensorRT
yolov5s.mlmodel # CoreML (macOS only)
yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb # TensorFlow GraphDef
yolov5s.tflite # TensorFlow Lite
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
yolov5s_paddle_model # PaddlePaddle
```
`val.py` runs validation on exported models:
```bash
python val.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with dnn=True
yolov5s_openvino_model # OpenVINO
yolov5s.engine # TensorRT
yolov5s.mlmodel # CoreML (macOS Only)
yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb # TensorFlow GraphDef
yolov5s.tflite # TensorFlow Lite
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
yolov5s_paddle_model # PaddlePaddle
```
Use PyTorch Hub with exported YOLOv5 models:
```python
import torch
# Model
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.pt")
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.torchscript ") # TorchScript
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.onnx") # ONNX Runtime
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s_openvino_model") # OpenVINO
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.engine") # TensorRT
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.mlmodel") # CoreML (macOS Only)
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s_saved_model") # TensorFlow SavedModel
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.pb") # TensorFlow GraphDef
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.tflite") # TensorFlow Lite
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s_edgetpu.tflite") # TensorFlow Edge TPU
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s_paddle_model") # PaddlePaddle
# Images
img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list
# Inference
results = model(img)
# Results
results.print() # or .show(), .save(), .crop(), .pandas(), etc.
```
## OpenCV DNN inference
[OpenCV](https://www.ultralytics.com/glossary/opencv) inference with ONNX models:
```bash
python export.py --weights yolov5s.pt --include onnx
python detect.py --weights yolov5s.onnx --dnn # detect
python val.py --weights yolov5s.onnx --dnn # validate
```
## C++ Inference
YOLOv5 OpenCV DNN C++ inference on exported ONNX model examples:
- [https://github.com/Hexmagic/ONNX-yolov5/blob/master/src/test.cpp](https://github.com/Hexmagic/ONNX-yolov5/blob/master/src/test.cpp)
- [https://github.com/doleron/yolov5-opencv-cpp-python](https://github.com/doleron/yolov5-opencv-cpp-python)
YOLOv5 OpenVINO C++ inference examples:
- [https://github.com/dacquaviva/yolov5-openvino-cpp-python](https://github.com/dacquaviva/yolov5-openvino-cpp-python)
- [https://github.com/UNeedCryDear/yolov5-seg-opencv-dnn-cpp](https://github.com/UNeedCryDear/yolov5-seg-opencv-onnxruntime-cpp)
## TensorFlow.js Web Browser Inference
- [https://aukerul-shuvo.github.io/YOLOv5_TensorFlow-JS/](https://aukerul-shuvo.github.io/YOLOv5_TensorFlow-JS/)
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
- **Free GPU Notebooks**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
- **Docker**: [Docker Quickstart Guide](../environments/docker_image_quickstart_tutorial.md) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
## Project Status
<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
This badge indicates that all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are successfully passing. These CI tests rigorously check the functionality and performance of YOLOv5 across various key aspects: [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py), and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py). They ensure consistent and reliable operation on macOS, Windows, and Ubuntu, with tests conducted every 24 hours and upon each new commit.
docs/en/yolov5/tutorials/model_pruning_and_sparsity.md 0 → 100644
View file @ a74dc9a0
---
comments: true
description: Learn how to prune YOLOv5 models for improved performance. Follow this step-by-step guide to optimize your YOLOv5 models effectively.
keywords: YOLOv5 pruning, model pruning, YOLOv5 optimization, YOLOv5 guide, machine learning pruning
---
📚 This guide explains how to apply **pruning** to YOLOv5 🚀 models.
## Before You Start
Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
```bash
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
```
## Test Normally
Before pruning we want to establish a baseline performance to compare to. This command tests YOLOv5x on COCO val2017 at image size 640 pixels. `yolov5x.pt` is the largest and most accurate model available. Other options are `yolov5s.pt`, `yolov5m.pt` and `yolov5l.pt`, or you own checkpoint from training a custom dataset `./weights/best.pt`. For details on all available models please see our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints).
```bash
python val.py --weights yolov5x.pt --data coco.yaml --img 640 --half
```
Output:
```shell
val: data=/content/yolov5/data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False
YOLOv5 🚀 v6.0-224-g4c40933 torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
Fusing layers...
Model Summary: 444 layers, 86705005 parameters, 0 gradients
val: Scanning '/content/datasets/coco/val2017.cache' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:00<?, ?it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:12<00:00, 2.16it/s]
all 5000 36335 0.732 0.628 0.683 0.496
Speed: 0.1ms pre-process, 5.2ms inference, 1.7ms NMS per image at shape (32, 3, 640, 640) # <--- base speed
Evaluating pycocotools mAP... saving runs/val/exp2/yolov5x_predictions.json...
...
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.507 # <--- base mAP
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.689
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.552
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.345
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.559
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.652
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.381
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.630
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.682
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.526
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.731
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.829
Results saved to runs/val/exp
```
## Test YOLOv5x on COCO (0.30 sparsity)
We repeat the above test with a pruned model by using the `torch_utils.prune()` command. We update `val.py` to prune YOLOv5x to 0.3 sparsity:
<img width="894" alt="Screenshot 2022-02-02 at 22 54 18" src="https://github.com/ultralytics/docs/releases/download/0/sparsity-test-yolov5x-coco.avif">
30% pruned output:
```bash
val: data=/content/yolov5/data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False
YOLOv5 🚀 v6.0-224-g4c40933 torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
Fusing layers...
Model Summary: 444 layers, 86705005 parameters, 0 gradients
Pruning model... 0.3 global sparsity
val: Scanning '/content/datasets/coco/val2017.cache' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:00<?, ?it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:11<00:00, 2.19it/s]
all 5000 36335 0.724 0.614 0.671 0.478
Speed: 0.1ms pre-process, 5.2ms inference, 1.7ms NMS per image at shape (32, 3, 640, 640) # <--- prune mAP
Evaluating pycocotools mAP... saving runs/val/exp3/yolov5x_predictions.json...
...
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.489 # <--- prune mAP
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.677
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.537
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.334
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.542
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.635
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.370
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.612
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.664
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.496
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.722
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.803
Results saved to runs/val/exp3
```
In the results we can observe that we have achieved a **sparsity of 30%** in our model after pruning, which means that 30% of the model's weight parameters in `nn.Conv2d` layers are equal to 0. **Inference time is essentially unchanged**, while the model's **AP and AR scores a slightly reduced**.
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
- **Free GPU Notebooks**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
- **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md)
- **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md)
- **Docker**: [Docker Quickstart Guide](../environments/docker_image_quickstart_tutorial.md) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
## Project Status
<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
This badge indicates that all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are successfully passing. These CI tests rigorously check the functionality and performance of YOLOv5 across various key aspects: [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py), and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py). They ensure consistent and reliable operation on macOS, Windows, and Ubuntu, with tests conducted every 24 hours and upon each new commit.
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