README.md



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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.


  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 test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt


April 11, 2021: v5.0 release: YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations.

January 5, 2021: v4.0 release: nn.SiLU() activations, Weights & Biases logging, PyTorch Hub integration.

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.


Pretrained Checkpoints


Model
size
^(pixels)

mAP^val
0.5:0.95

mAP^{test
0.5:0.95}

mAP^val
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)


AP^test 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


Speed_GPU 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


Train Custom Data  🚀 RECOMMENDED

Tips for Best Training Results  ☘️ RECOMMENDED

Weights & Biases Logging  🌟 NEW

Supervisely Ecosystem  🌟 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

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 and Kaggle notebooks with free GPU:  


Google Cloud Deep Learning VM. See GCP Quickstart Guide


Amazon Deep Learning AMI. See AWS Quickstart Guide


Docker Image. See Docker Quickstart Guide 


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

Inference with YOLOv5 and PyTorch Hub:

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

# Image
img = 'https://ultralytics.com/images/zidane.jpg'

# Inference
results = model(img)
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://ultralytics.com. 


Contact

Issues should be raised directly in the repository. For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.