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YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

To request an Enterprise License please complete the form at Ultralytics Licensing.

Ultralytics Live Session

We're excited to announce our very first [Ultralytics Live Session](https://www.youtube.com/@Ultralytics/streams) ✨ streaming live at https://youtu.be/KS4weDInJYs on **Tuesday, November 29th at 16:00 CET** with [@thepycoder](https://github.com/thepycoder) of [ClearML](https://cutt.ly/yolov5-readme-clearml). Glenn and Victor will discuss all things experiment tracking and how you can maximize the benefits of the new YOLOv5 + ClearML [integration](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml). 🔥

Segmentation ⭐ NEW

Our new YOLOv5 release v7.0 instance segmentation models are the fastest and most accurate in the world, beating all current SOTA benchmarks. We've made them super simple to train, validate and deploy. See full details in our Release Notes and visit our YOLOv5 Segmentation Colab Notebook for quickstart tutorials.

Segmentation Usage Examples  

### Train YOLOv5 segmentation training supports auto-download COCO128-seg segmentation dataset with `--data coco128-seg.yaml` argument and manual download of COCO-segments dataset with `bash data/scripts/get_coco.sh --train --val --segments` and then `python train.py --data coco.yaml`. ```bash # Single-GPU python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 # Multi-GPU DDP python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3 ``` ### Val Validate YOLOv5s-seg mask mAP on COCO dataset: ```bash bash data/scripts/get_coco.sh --val --segments # download COCO val segments split (780MB, 5000 images) python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate ``` ### Predict Use pretrained YOLOv5m-seg.pt to predict bus.jpg: ```bash python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpg ``` ```python model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5m-seg.pt') # load from PyTorch Hub (WARNING: inference not yet supported) ```  |  --- |--- ### Export Export YOLOv5s-seg model to ONNX and TensorRT: ```bash python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0 ```

Documentation

See the YOLOv5 Docs for full documentation on training, testing and deployment. See below for quickstart examples.

Training

The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) results. [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). Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB. ```bash python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 yolov5s 64 yolov5m 40 yolov5l 24 yolov5x 16 ```

Integrations

Ultralytics HUB

Ultralytics HUB is our ⭐ NEW no-code solution to visualize datasets, train YOLOv5 🚀 models, and deploy to the real world in a seamless experience. Get started for Free now!

Why YOLOv5

YOLOv5 has been designed to be super easy to get started and simple to learn. We prioritize real-world results.

Pretrained Checkpoints

Classification ⭐ NEW

YOLOv5 release v6.2 brings support for classification model training, validation and deployment! See full details in our Release Notes and visit our YOLOv5 Classification Colab Notebook for quickstart tutorials.

Classification Usage Examples  

### Train YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`. ```bash # Single-GPU python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128 # Multi-GPU DDP python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 ``` ### Val Validate YOLOv5m-cls accuracy on ImageNet-1k dataset: ```bash bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ``` ### Predict Use pretrained YOLOv5s-cls.pt to predict bus.jpg: ```bash python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg ``` ```python model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt') # load from PyTorch Hub ``` ### Export Export a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT: ```bash python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224 ```

Environments

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Contribute

We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our Contributing Guide to get started, and fill out the YOLOv5 Survey to send us feedback on your experiences. Thank you to all our contributors!

License

YOLOv5 is available under two different licenses:

• GPL-3.0 License: See LICENSE file for details.
• Enterprise License: Provides greater flexibility for commercial product development without the open-source requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at Ultralytics Licensing.

Contact

For YOLOv5 bugs and feature requests please visit GitHub Issues. For professional support please Contact Us.