This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. All code and models are under active development, and are subject to modification or deletion without notice. Use at your own risk.
** 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.
- August 13, 2020: v3.0 release: nn.Hardswish() activations, data autodownload, native AMP.
- July 23, 2020: v2.0 release: improved model definition, training and mAP.
- June 22, 2020: PANet updates: new heads, reduced parameters, improved speed and mAP 364fcfd.
- June 19, 2020: FP16 as new default for smaller checkpoints and faster inference d4c6674.
- June 9, 2020: CSP updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
- May 27, 2020: Public release. YOLOv5 models are SOTA among all known YOLO implementations.
- April 1, 2020: Start development of future compound-scaled YOLOv3/YOLOv4-based PyTorch models.
| Model | APval | APtest | AP50 | SpeedGPU | FPSGPU | params | FLOPS | |
|---|---|---|---|---|---|---|---|---|
| YOLOv5s | 37.0 | 37.0 | 56.2 | 2.4ms | 416 | 7.5M | 13.2B | |
| YOLOv5m | 44.3 | 44.3 | 63.2 | 3.4ms | 294 | 21.8M | 39.4B | |
| YOLOv5l | 47.7 | 47.7 | 66.5 | 4.4ms | 227 | 47.8M | 88.1B | |
| YOLOv5x | 49.2 | 49.2 | 67.7 | 6.9ms | 145 | 89.0M | 166.4B | |
| YOLOv5x + TTA | 50.8 | 50.8 | 68.9 | 25.5ms | 39 | 89.0M | 354.3B | |
| YOLOv3-SPP | 45.6 | 45.5 | 65.2 | 4.5ms | 222 | 63.0M | 118.0B |
** APtest denotes COCO test-dev2017 server results, all other AP results in the table denote val2017 accuracy.
** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by python test.py --data coco.yaml --img 640 --conf 0.001
** SpeedGPU measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP n1-standard-16 instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by python test.py --data coco.yaml --img 640 --conf 0.1
** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
** Test Time Augmentation (TTA) runs at 3 image sizes. Reproduce by python test.py --data coco.yaml --img 832 --augment
Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.6. To install run:
$ pip install -r requirements.txt- Train Custom Data
- Multi-GPU Training
- PyTorch Hub
- ONNX and TorchScript Export
- Test-Time Augmentation (TTA)
- Model Ensembling
- Model Pruning/Sparsity
- Hyperparameter Evolution
- TensorRT Deployment
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Google Colab Notebook with free GPU:
- Kaggle Notebook with free GPU: https://www.kaggle.com/ultralytics/yolov5
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Docker Image https://hub.docker.com/r/ultralytics/yolov5. See Docker Quickstart Guide
detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to inference/output.
$ python detect.py --source 0 # webcam
file.jpg # image
file.mp4 # video
path/ # directory
path/*.jpg # glob
rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream
rtmp://192.168.1.105/live/test # rtmp stream
http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http streamTo run inference on example images in inference/images:
$ python detect.py --source inference/images --weights yolov5s.pt --conf 0.25
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, output='inference/output', save_conf=False, save_txt=False, source='inference/images', update=False, view_img=False, weights='yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16160MB)
Downloading https://github.com/ultralytics/yolov5/releases/download/v3.0/yolov5s.pt to yolov5s.pt... 100%|██████████████| 14.5M/14.5M [00:00<00:00, 21.3MB/s]
Fusing layers...
Model Summary: 140 layers, 7.45958e+06 parameters, 0 gradients
image 1/2 yolov5/inference/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.013s)
image 2/2 yolov5/inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.013s)
Results saved to yolov5/inference/output
Done. (0.124s)
To run batched inference with YOLOv5 and PyTorch Hub:
import torch
from PIL import Image
# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True).fuse().eval() # yolov5s.pt
model = model.autoshape() # for autoshaping of PIL/cv2/np inputs and NMS
# Images
img1 = Image.open('zidane.jpg')
img2 = Image.open('bus.jpg')
imgs = [img1, img2] # batched list of images
# Inference
prediction = model(imgs, size=640) # includes NMSDownload COCO and run command below. 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
