• Documentation
• Quick Start Examples
• Environments
• Integrations
• Why YOLOv5
• • Pretrained Checkpoints
• Contribute
• Contact

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
                            img.jpg  # image
                            vid.mp4  # video
                            path/  # directory
                            path/*.jpg  # glob
                            'https://youtu.be/Zgi9g1ksQHc'  # 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
• TFLite, ONNX, CoreML, TensorRT 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 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)

• COCO AP val denotes mAP@0.5:0.95 metric measured on the 5000-image COCO val2017 dataset over various inference sizes from 256 to 1536.
• GPU Speed measures average inference time per image on COCO val2017 dataset using a AWS p3.2xlarge V100 instance at batch-size 32.
• EfficientDet data from google/automl at batch size 8.
• Reproduce by python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt

Pretrained Checkpoints

Model	size (pixels)	mAPval 0.5:0.95	mAPval 0.5	Speed CPU b1 (ms)	Speed V100 b1 (ms)	Speed V100 b32 (ms)	params (M)	FLOPs @640 (B)
YOLOv5n	640	28.4	46.0	45	6.3	0.6	1.9	4.5
YOLOv5s	640	37.2	56.0	98	6.4	0.9	7.2	16.5
YOLOv5m	640	45.2	63.9	224	8.2	1.7	21.2	49.0
YOLOv5l	640	48.8	67.2	430	10.1	2.7	46.5	109.1
YOLOv5x	640	50.7	68.9	766	12.1	4.8	86.7	205.7
YOLOv5n6	1280	34.0	50.7	153	8.1	2.1	3.2	4.6
YOLOv5s6	1280	44.5	63.0	385	8.2	3.6	12.6	16.8
YOLOv5m6	1280	51.0	69.0	887	11.1	6.8	35.7	50.0
YOLOv5l6	1280	53.6	71.6	1784	15.8	10.5	76.7	111.4
YOLOv5x6 + TTA	1280 1536	54.7 55.4	72.4 72.3	3136 -	26.2 -	19.4 -	140.7 -	209.8 -

Table Notes (click to expand)

• All checkpoints are trained to 300 epochs with default settings and hyperparameters.
• mAP^val values are for single-model single-scale on COCO val2017 dataset.
  Reproduce by python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
• Speed averaged over COCO val images using a AWS p3.2xlarge instance. NMS times (~1 ms/img) not included.
  Reproduce by python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45
• TTA Test Time Augmentation includes reflection and scale augmentations.
  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 send us feedback on your experiences. Thank you to all our contributors!

Contact

For YOLOv5 bugs and feature requests please visit GitHub Issues. For business inquiries or
professional support requests please visit https://ultralytics.com/contact.