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

from PIL import Image
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 = Image.open("yolov5/data/images/bus.jpg")
image2 = Image.open("yolov5/data/images/zidane.jpg")

# perform inference
results = yolov5.predict(image1)

# perform inference with higher 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/')

Tutorials

• Train Custom Data
• Transfer Learning with Frozen Layers

Scripts

You can download and use train.py, detect.py and test.py scripts 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).

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

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
                            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 data/images:

$ python detect.py --source 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: