Documentation
See the YOLOv5 Docs for full documentation on training, testing and deployment.
Quick Start Examples
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 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.
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
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
- 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 export your custom datasets directly to YOLOv5 for training with Roboflow |
Why YOLOv5
- 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) | mAPval0.5:0.95 | mAPval0.5 | SpeedCPU b1(ms) | SpeedV100 b1(ms) | SpeedV100 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 | 16.8 | 12.6 |
| 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 | 76.8 | 111.4 |
| YOLOv5x6+ TTA | 12801536 | 54.755.4 | 72.472.3 | 3136- | 26.2- | 19.4- | 140.7- | 209.8- |
- All checkpoints are trained to 300 epochs with default settings and hyperparameters.
- mAPval 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.