ultralytics_yolov5.md

layout	background-class	body-class	category	title	summary	image	author	tags	github-link	github-id	featured_image_1	featured_image_2	accelerator
hub_detail	hub-background	hub	researchers	YOLOv5	YOLOv5 in PyTorch > ONNX > CoreML > TFLite	ultralytics_yolov5_img0.jpg	Ultralytics LLC	vision scriptable	https://github.com/ultralytics/yolov5	ultralytics/yolov5	ultralytics_yolov5_img1.jpg	ultralytics_yolov5_img2.png	cuda-optional

Before You Start

Start from a Python>=3.8 environment with PyTorch>=1.7 installed. To install PyTorch see https://pytorch.org/get-started/locally/. To install YOLOv5 dependencies:

pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt  # install dependencies

Model Description

 

YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite.

Model	size	APval	APtest	AP50	SpeedV100	FPSV100		params	GFLOPS
YOLOv5s	640	36.8	36.8	55.6	2.2ms	455		7.3M	17.0
YOLOv5m	640	44.5	44.5	63.1	2.9ms	345		21.4M	51.3
YOLOv5l	640	48.1	48.1	66.4	3.8ms	264		47.0M	115.4
YOLOv5x	640	50.1	50.1	68.7	6.0ms	167		87.7M	218.8
YOLOv5x + TTA	832	51.9	51.9	69.6	24.9ms	40		87.7M	1005.3

** 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](https://github.com/google/automl) at batch size 8.

Load From PyTorch Hub

This simple example loads a pretrained YOLOv5s model from PyTorch Hub as model and passes two image URLs for batched inference.

import torch

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

# Images
dir = 'https://github.com/ultralytics/yolov5/raw/master/data/images/'
imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')]  # batched list of images

# Inference
results = model(imgs)

# Results
results.print()  
results.save()  # or .show()

# Data
print(results.xyxy[0])  # print img1 predictions (pixels)
#                   x1           y1           x2           y2   confidence        class
# tensor([[7.50637e+02, 4.37279e+01, 1.15887e+03, 7.08682e+02, 8.18137e-01, 0.00000e+00],
#         [9.33597e+01, 2.07387e+02, 1.04737e+03, 7.10224e+02, 5.78011e-01, 0.00000e+00],
#         [4.24503e+02, 4.29092e+02, 5.16300e+02, 7.16425e+02, 5.68713e-01, 2.70000e+01]])

For YOLOv5 PyTorch Hub inference with PIL, OpenCV, Numpy or PyTorch inputs please see the full YOLOv5 PyTorch Hub Tutorial.

Citation

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 [email protected].