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 using 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)
- APtest denotes COCO test-dev2017 server results, all other AP results denote val2017 accuracy.
- AP values are for single-model single-scale unless otherwise noted. Reproduce mAP
by
python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65 - SpeedGPU averaged over 5000 COCO val2017 images using a
GCP n1-standard-16 V100 instance, and
includes FP16 inference, postprocessing and NMS. Reproduce speed
by
python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half - All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
- Test Time Augmentation (TTA) includes reflection and scale
augmentation. Reproduce TTA 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.
Contact
For issues running YOLOv5 please visit GitHub Issues. For business or professional support requests please visit https://ultralytics.com/contact.