yolov5 5.0.1

Packaged version of the Yolov5 object detector

Packaged YOLOv5 Object Detector

You can finally install YOLOv5 object detector using pip and integrate into your project easily.

Overview

This package is up-to-date with the latest release of ultralytics/yolov5.

Installation

• Install yolov5 using pip (for Python >=3.7):

pip install yolov5

• Install yolov5 using pip (for Python 3.6):

pip install "numpy>=1.18.5,<1.20" "matplotlib>=3.2.2,<4"
pip install yolov5

Basic Usage

import yolov5

# model
model = yolov5.load('yolov5s')

# image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# inference
results = model(img)

# inference with larger input size
results = model(img, size=1280)

# inference with test time augmentation
results = model(img, augment=True)

# show results
results.show()

# save results
results.save(save_dir='results/')

Alternative Usage

from yolov5 import YOLOv5

# set model params
model_path = "yolov5/weights/yolov5s.pt" # it automatically downloads yolov5s model to given path
device = "cuda" # or "cpu"

# init yolov5 model
yolov5 = YOLOv5(model_path, device)

# load images
image1 = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
image2 = 'https://github.com/ultralytics/yolov5/blob/master/data/images/bus.jpg'

# perform inference
results = yolov5.predict(image1)

# perform inference with larger input size
results = yolov5.predict(image1, size=1280)

# perform inference with test time augmentation
results = yolov5.predict(image1, augment=True)

# perform inference on multiple images
results = yolov5.predict([image1, image2], size=1280, augment=True)

# show detection bounding boxes on image
results.show()

# save results into "results/" folder
results.save(save_dir='results/')

Scripts

You can call yolo_train, yolo_detect and yolo_test commands after installing the package via pip:

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).

$ yolo_train --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
                                    yolov5m                                40
                                    yolov5l                                24
                                    yolov5x                                16

Inference

yolo_detect command runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect.

$ yolo_detect --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 stream

To run inference on example images in yolov5/data/images:

$ yolo_detect --source yolov5/data/images --weights yolov5s.pt --conf 0.25

Status

Builds for the latest commit for Windows/Linux/MacOS with Python3.6/3.7/3.8:

Status for the train/detect/test scripts: