YOLOv5 ๐ 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.
See the YOLOv5 Docs for full documentation on training, testing and deployment.
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
![](https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png)
Tutorials
- Train Custom Dataย ๐ RECOMMENDED
- Tips for Best Training Resultsย โ๏ธ RECOMMENDED
- Weights & Biases Loggingย ๐ NEW
- Roboflow for Datasets, Labeling, and Active Learningย ๐ NEW
- Multi-GPU Training
- PyTorch Hubย โญ NEW
- TorchScript, ONNX, CoreML Export ๐
- Test-Time Augmentation (TTA)
- Model Ensembling
- Model Pruning/Sparsity
- Hyperparameter Evolution
- Transfer Learning with Frozen Layersย โญ NEW
- TensorRT Deployment
Get started in seconds with our verified environments. Click each icon below for details.
Weights and Biases | Roboflow โญ 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 |
Figure Notes (click to expand)
- COCO AP val denotes [email protected]: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
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 | 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 |
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.
- mAPval values are for single-model single-scale on COCO val2017 dataset.
Reproduce bypython 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 bypython val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45
- TTA Test Time Augmentation includes reflection and scale augmentations.
Reproduce bypython val.py --data coco.yaml --img 1536 --iou 0.7 --augment
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!
For issues running YOLOv5 please visit GitHub Issues. For business or professional support requests please visit https://ultralytics.com/contact.