yolov5-pip

• Update to ultralytics/yolov5 24.04.21

• Update to ultralytics/yolov5 24.04.21

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/')

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 data/images:

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

• update yolov5.utils.google_utils.attempt_download

• fix windows installation

• include models/*.yml files in package

• fixes inference from string image filepath https://github.com/fcakyon/yolov5-pip/issues/9

• Update common, torch_utils, yolo, plots files to latest.

• add automatic folder directory creation
• fix google utils
• fix model weight check

• fully functional pip package version of ultralytics/yolov5 v4.0 release

• fix train.py and detect.py scripts

• update python3.6 setup
• fix torch.load() error
• fix import errors
• refactorize model loading
• update tests

• fix pypi wheel for python 3.6
• fix a packaging bug
• add YOLOv5 wrapper class