YOLOv5

YOLOv5 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.

Quick Start Examples

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
Tutorials

Environments and Integrations

Get started in seconds with our verified environments and integrations,
including Weights & Biases for automatic YOLOv5 experiment
logging. Click each icon below for details.

Compete and Win

We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with $10,000 in cash prizes!

Why YOLOv5

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

Pretrained Checkpoints

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 63.1 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)
  • 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 val.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 val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half
  • 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 val.py --data coco.yaml --img 1536 --iou 0.7 --augment

GitHub

https://github.com/acai66/yolov5_rotation