This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. All code and models are under active development, and are subject to modification or deletion without notice. Use at your own risk.
| 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 |
63.3 |
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 test.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 test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45
- 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 test.py --data coco.yaml --img 1536 --iou 0.7 --augment
Requirements
Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:
$ pip install -r requirements.txt
Tutorials
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
Inference
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 video
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
To run inference on example images in data/images:
$ python detect.py --source data/images --weights yolov5s.pt --conf 0.25
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt'])
YOLOv5 v4.0-96-g83dc1b4 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)
Fusing layers...
Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS
image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.010s)
image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.011s)
Results saved to runs/detect/exp2
Done. (0.103s)
PyTorch Hub
To run batched inference with YOLOv5 and PyTorch Hub:
import torch
# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
# Images
dir = 'https://github.com/ultralytics/yolov5/raw/master/data/images/'
imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')] # batch of images
# Inference
results = model(imgs)
results.print() # or .show(), .save()
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
Citation
About Us
Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including:
- Cloud-based AI systems operating on hundreds of HD video streams in realtime.
- Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
- Custom data training, hyperparameter evolution, and model exportation to any destination.
For business inquiries and professional support requests please visit us at https://www.ultralytics.com.
Contact
Issues should be raised directly in the repository. For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.
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修改训练嵌入参数
 
