yolov5 5.0.3

Packaged version of the Yolov5 object detector

packaged ultralytics/yolov5

pip install yolov5

Overview

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

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: