YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

Documentation

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

Quick Start Examples

Install

Python>=3.6.0 is required with all requirements.txt installed including PyTorch>=1.7:

$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt

Inference

Inference with YOLOv5 and PyTorch Hub. Models automatically download from the latest YOLOv5 release.

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, custom

# 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.

Inference with detect.py

detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect.

$ python detect.py --source 0  # webcam
                            file.jpg  # image 
                            file.mp4  # video
                            path/  # directory
                            path/*.jpg  # glob
                            'https://youtu.be/NUsoVlDFqZg'  # YouTube
                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Training

Run commands below to reproduce results on COCO dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices).

$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
                                         yolov5m                                40
                                         yolov5l                                24
                                         yolov5x                                16

Tutorials

• Train Custom Data  🚀 RECOMMENDED
• Tips for Best Training Results  ☘️ RECOMMENDED
• Weights & Biases Logging  🌟 NEW
• Roboflow for Datasets, Labeling, and Active Learning  🌟 NEW
• Multi-GPU Training
• PyTorch Hub  ⭐ NEW
• TorchScript, ONNX, CoreML Export 🚀
• Test-Time Augmentation (TTA)
• Model Ensembling
• Model Pruning/Sparsity
• Hyperparameter Evolution
• Transfer Learning with Frozen Layers  ⭐ NEW
• TensorRT Deployment

Environments

Get started in seconds with our verified environments. Click each icon below for details.

Integrations

Weights and Biases	Roboflow - ⭐ NEW
Automatically track and visualize all your YOLOv5 training runs in the cloud with Weights & Biases	Label and automatically export your custom datasets directly to YOLOv5 for training with Roboflow

Why YOLOv5

YOLOv5-P5 640 Figure (click to expand)

Figure Notes (click to expand)

• GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
• EfficientDet data from google/automl at batch size 8.
• Reproduce by python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt

Pretrained Checkpoints

Model	size (pixels)	mAPval 0.5:0.95	mAPtest 0.5:0.95	mAPval 0.5	Speed V100 (ms)		params (M)	FLOPs 640 (B)
YOLOv5s	640	36.7	36.7	55.4	2.0		7.3	17.0
YOLOv5m	640	44.5	44.5	63.1	2.7		21.4	51.3
YOLOv5l	640	48.2	48.2	66.9	3.8		47.0	115.4
YOLOv5x	640	50.4	50.4	68.8	6.1		87.7	218.8
YOLOv5s6	1280	43.3	43.3	61.9	4.3		12.7	17.4
YOLOv5m6	1280	50.5	50.5	68.7	8.4		35.9	52.4
YOLOv5l6	1280	53.4	53.4	71.1	12.3		77.2	117.7
YOLOv5x6	1280	54.4	54.4	72.0	22.4		141.8	222.9
YOLOv5x6 TTA	1280	55.0	55.0	72.0	70.8		-	-

Table Notes (click to expand)

• All checkpoints are trained to 300 epochs with default settings and hyperparameters.
• AP^test denotes COCO test-dev2017 server results, all other AP results denote val2017 accuracy.
• mAP values are for single-model single-scale unless otherwise noted.
  Reproduce by python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
• Speed averaged over 5000 COCO val2017 images using a GCP n1-standard-16 V100 instance, and includes FP16 inference, postprocessing and NMS.
  Reproduce by python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half
• TTA Test Time Augmentation includes reflection and scale.
  Reproduce by python val.py --data coco.yaml --img 1536 --iou 0.7 --augment

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 provide thoughts and feedback on your experience with YOLOv5. Thank you!

Contact

For issues running YOLOv5 please visit GitHub Issues. For business or professional support requests please visit https://ultralytics.com/contact.