---
comments: true
description: Learn how to load YOLOv5 from PyTorch Hub for seamless model inference and customization. Follow our step-by-step guide at Ultralytics Docs.
keywords: YOLOv5, PyTorch Hub, model loading, Ultralytics, object detection, machine learning, AI, tutorial, inference
---

📚 This guide explains how to load YOLOv5 🚀 from [PyTorch](https://www.ultralytics.com/glossary/pytorch) Hub at [https://pytorch.org/hub/ultralytics_yolov5](https://pytorch.org/hub/ultralytics_yolov5/).

## Before You Start

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
pip install -r https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt
```

💡 ProTip: Cloning [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5) is **not** required 😃

## Load YOLOv5 with PyTorch Hub

### Simple Example

This example loads a pretrained YOLOv5s model from PyTorch Hub as `model` and passes an image for inference. `'yolov5s'` is the lightest and fastest YOLOv5 model. For details on all available models please see the [README](https://github.com/ultralytics/yolov5#pretrained-checkpoints).

```python
import torch

# Model
model = torch.hub.load("ultralytics/yolov5", "yolov5s")

# Image
im = "https://ultralytics.com/images/zidane.jpg"

# Inference
results = model(im)

results.pandas().xyxy[0]
#      xmin    ymin    xmax   ymax  confidence  class    name
# 0  749.50   43.50  1148.0  704.5    0.874023      0  person
# 1  433.50  433.50   517.5  714.5    0.687988     27     tie
# 2  114.75  195.75  1095.0  708.0    0.624512      0  person
# 3  986.00  304.00  1028.0  420.0    0.286865     27     tie
```

### Detailed Example

This example shows **batched inference** with **PIL** and **[OpenCV](https://www.ultralytics.com/glossary/opencv)** image sources. `results` can be **printed** to console, **saved** to `runs/hub`, **showed** to screen on supported environments, and returned as **tensors** or **pandas** dataframes.

```python
import cv2
import torch
from PIL import Image

# Model
model = torch.hub.load("ultralytics/yolov5", "yolov5s")

# Images
for f in "zidane.jpg", "bus.jpg":
    torch.hub.download_url_to_file("https://ultralytics.com/images/" + f, f)  # download 2 images
im1 = Image.open("zidane.jpg")  # PIL image
im2 = cv2.imread("bus.jpg")[..., ::-1]  # OpenCV image (BGR to RGB)

# Inference
results = model([im1, im2], size=640)  # batch of images

# Results
results.print()
results.save()  # or .show()

results.xyxy[0]  # im1 predictions (tensor)
results.pandas().xyxy[0]  # im1 predictions (pandas)
#      xmin    ymin    xmax   ymax  confidence  class    name
# 0  749.50   43.50  1148.0  704.5    0.874023      0  person
# 1  433.50  433.50   517.5  714.5    0.687988     27     tie
# 2  114.75  195.75  1095.0  708.0    0.624512      0  person
# 3  986.00  304.00  1028.0  420.0    0.286865     27     tie
```

<img src="https://github.com/ultralytics/docs/releases/download/0/yolo-inference-results-zidane.avif" width="500" alt="YOLO inference results on zidane.jpg">
<img src="https://github.com/ultralytics/docs/releases/download/0/yolo-inference-results-on-bus.avif" width="300" alt="YOLO inference results on bus.jpg">

For all inference options see YOLOv5 `AutoShape()` forward [method](https://github.com/ultralytics/yolov5/blob/30e4c4f09297b67afedf8b2bcd851833ddc9dead/models/common.py#L243-L252).

### Inference Settings

YOLOv5 models contain various inference attributes such as **confidence threshold**, **IoU threshold**, etc. which can be set by:

```python
model.conf = 0.25  # NMS confidence threshold
iou = 0.45  # NMS IoU threshold
agnostic = False  # NMS class-agnostic
multi_label = False  # NMS multiple labels per box
classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
max_det = 1000  # maximum number of detections per image
amp = False  # Automatic Mixed Precision (AMP) inference

results = model(im, size=320)  # custom inference size
```

### Device

Models can be transferred to any device after creation:

```python
model.cpu()  # CPU
model.cuda()  # GPU
model.to(device)  # i.e. device=torch.device(0)
```

Models can also be created directly on any `device`:

```python
model = torch.hub.load("ultralytics/yolov5", "yolov5s", device="cpu")  # load on CPU
```

💡 ProTip: Input images are automatically transferred to the correct model device before inference.

### Silence Outputs

Models can be loaded silently with `_verbose=False`:

```python
model = torch.hub.load("ultralytics/yolov5", "yolov5s", _verbose=False)  # load silently
```

### Input Channels

To load a pretrained YOLOv5s model with 4 input channels rather than the default 3:

```python
model = torch.hub.load("ultralytics/yolov5", "yolov5s", channels=4)
```

In this case the model will be composed of pretrained weights **except for** the very first input layer, which is no longer the same shape as the pretrained input layer. The input layer will remain initialized by random weights.

### Number of Classes

To load a pretrained YOLOv5s model with 10 output classes rather than the default 80:

```python
model = torch.hub.load("ultralytics/yolov5", "yolov5s", classes=10)
```

In this case the model will be composed of pretrained weights **except for** the output layers, which are no longer the same shape as the pretrained output layers. The output layers will remain initialized by random weights.

### Force Reload

If you run into problems with the above steps, setting `force_reload=True` may help by discarding the existing cache and force a fresh download of the latest YOLOv5 version from PyTorch Hub.

```python
model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True)  # force reload
```

### Screenshot Inference

To run inference on your desktop screen:

```python
import torch
from PIL import ImageGrab

# Model
model = torch.hub.load("ultralytics/yolov5", "yolov5s")

# Image
im = ImageGrab.grab()  # take a screenshot

# Inference
results = model(im)
```

### Multi-GPU Inference

YOLOv5 models can be loaded to multiple GPUs in parallel with threaded inference:

```python
import threading

import torch


def run(model, im):
    """Performs inference on an image using a given model and saves the output; model must support `.save()` method."""
    results = model(im)
    results.save()


# Models
model0 = torch.hub.load("ultralytics/yolov5", "yolov5s", device=0)
model1 = torch.hub.load("ultralytics/yolov5", "yolov5s", device=1)

# Inference
threading.Thread(target=run, args=[model0, "https://ultralytics.com/images/zidane.jpg"], daemon=True).start()
threading.Thread(target=run, args=[model1, "https://ultralytics.com/images/bus.jpg"], daemon=True).start()
```

### Training

To load a YOLOv5 model for training rather than inference, set `autoshape=False`. To load a model with randomly initialized weights (to train from scratch) use `pretrained=False`. You must provide your own training script in this case. Alternatively see our YOLOv5 [Train Custom Data Tutorial](./train_custom_data.md) for model training.

```python
import torch

model = torch.hub.load("ultralytics/yolov5", "yolov5s", autoshape=False)  # load pretrained
model = torch.hub.load("ultralytics/yolov5", "yolov5s", autoshape=False, pretrained=False)  # load scratch
```

### Base64 Results

For use with API services. See https://github.com/ultralytics/yolov5/pull/2291 and [Flask REST API](https://github.com/ultralytics/yolov5/tree/master/utils/flask_rest_api) example for details.

```python
results = model(im)  # inference

results.ims  # array of original images (as np array) passed to model for inference
results.render()  # updates results.ims with boxes and labels
for im in results.ims:
    buffered = BytesIO()
    im_base64 = Image.fromarray(im)
    im_base64.save(buffered, format="JPEG")
    print(base64.b64encode(buffered.getvalue()).decode("utf-8"))  # base64 encoded image with results
```

### Cropped Results

Results can be returned and saved as detection crops:

```python
results = model(im)  # inference
crops = results.crop(save=True)  # cropped detections dictionary
```

### Pandas Results

Results can be returned as [Pandas DataFrames](https://pandas.pydata.org/):

```python
results = model(im)  # inference
results.pandas().xyxy[0]  # Pandas DataFrame
```

<details>
  <summary>Pandas Output (click to expand)</summary>

```python
print(results.pandas().xyxy[0])
#      xmin    ymin    xmax   ymax  confidence  class    name
# 0  749.50   43.50  1148.0  704.5    0.874023      0  person
# 1  433.50  433.50   517.5  714.5    0.687988     27     tie
# 2  114.75  195.75  1095.0  708.0    0.624512      0  person
# 3  986.00  304.00  1028.0  420.0    0.286865     27     tie
```

</details>

### Sorted Results

Results can be sorted by column, i.e. to sort license plate digit detection left-to-right (x-axis):

```python
results = model(im)  # inference
results.pandas().xyxy[0].sort_values("xmin")  # sorted left-right
```

### Box-Cropped Results

Results can be returned and saved as detection crops:

```python
results = model(im)  # inference
crops = results.crop(save=True)  # cropped detections dictionary
```

### JSON Results

Results can be returned in JSON format once converted to `.pandas()` dataframes using the `.to_json()` method. The JSON format can be modified using the `orient` argument. See pandas `.to_json()` [documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html) for details.

```python
results = model(ims)  # inference
results.pandas().xyxy[0].to_json(orient="records")  # JSON img1 predictions
```

<details>
  <summary>JSON Output (click to expand)</summary>

```json
[
    {
        "xmin": 749.5,
        "ymin": 43.5,
        "xmax": 1148.0,
        "ymax": 704.5,
        "confidence": 0.8740234375,
        "class": 0,
        "name": "person"
    },
    {
        "xmin": 433.5,
        "ymin": 433.5,
        "xmax": 517.5,
        "ymax": 714.5,
        "confidence": 0.6879882812,
        "class": 27,
        "name": "tie"
    },
    {
        "xmin": 115.25,
        "ymin": 195.75,
        "xmax": 1096.0,
        "ymax": 708.0,
        "confidence": 0.6254882812,
        "class": 0,
        "name": "person"
    },
    {
        "xmin": 986.0,
        "ymin": 304.0,
        "xmax": 1028.0,
        "ymax": 420.0,
        "confidence": 0.2873535156,
        "class": 27,
        "name": "tie"
    }
]
```

</details>

## Custom Models

This example loads a custom 20-class [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml)-trained YOLOv5s model `'best.pt'` with PyTorch Hub.

```python
import torch

model = torch.hub.load("ultralytics/yolov5", "custom", path="path/to/best.pt")  # local model
model = torch.hub.load("path/to/yolov5", "custom", path="path/to/best.pt", source="local")  # local repo
```

## TensorRT, ONNX and OpenVINO Models

PyTorch Hub supports inference on most YOLOv5 export formats, including custom trained models. See [TFLite, ONNX, CoreML, TensorRT Export tutorial](./model_export.md) for details on exporting models.

💡 ProTip: **TensorRT** may be up to 2-5X faster than PyTorch on [**GPU benchmarks**](https://github.com/ultralytics/yolov5/pull/6963)
💡 ProTip: **ONNX** and **OpenVINO** may be up to 2-3X faster than PyTorch on [**CPU benchmarks**](https://github.com/ultralytics/yolov5/pull/6613)

```python
import torch

model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s.pt")  # PyTorch
model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s.torchscript")  # TorchScript
model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s.onnx")  # ONNX
model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s_openvino_model/")  # OpenVINO
model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s.engine")  # TensorRT
model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s.mlmodel")  # CoreML (macOS-only)
model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s.tflite")  # TFLite
model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s_paddle_model/")  # PaddlePaddle
```

## 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.
