Huge News!Announcing our $40M Series B led by Abstract Ventures.Learn More
Socket
Sign inDemoInstall
Socket

y5gg

Package Overview
Dependencies
Maintainers
1
Alerts
File Explorer

Advanced tools

Socket logo

Install Socket

Detect and block malicious and high-risk dependencies

Install

y5gg

Packaged version of the Yolov5 object detector

  • 6.0.7
  • PyPI
  • Socket score

Maintainers
1

packaged ultralytics/yolov5

pip install yolov5
total downloads monthly downloads pypi version
ci testing package testing

Overview

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

Install

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

Use from Python

Basic
import yolov5

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

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

# perform inference
results = model(img)

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

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

# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, x2, y1, y2
scores = predictions[:, 4]
categories = predictions[:, 5]

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

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

Alternative
from yolov5 import YOLOv5

# set model params
model_path = "yolov5/weights/yolov5s.pt"
device = "cuda:0" # 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)

# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, x2, y1, y2
scores = predictions[:, 4]
categories = predictions[:, 5]

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

# save results into "results/" folder
results.save(save_dir='results/')
Train/Detect/Test/Export
  • You can directly use these functions by importing them:
from yolov5 import train, val, detect, export

train.run(imgsz=640, data='coco128.yaml')
val.run(imgsz=640, data='coco128.yaml', weights='yolov5s.pt')
detect.run(imgsz=640)
export.run(imgsz=640, weights='yolov5s.pt')
  • You can pass any argument as input:
from yolov5 import detect

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

detect.run(source=img_url, weights="yolov5s6.pt", conf_thres=0.25, imgsz=640)

Use from CLI

You can call yolov5 train, yolov5 detect, yolov5 val and yolov5 export commands after installing the package via pip:

Training

Finetune one of the pretrained YOLOv5 models using your custom data.yaml:

$ yolov5 train --data data.yaml --weights yolov5s.pt --batch-size 16 --img 640
                                          yolov5m.pt              8
                                          yolov5l.pt              4
                                          yolov5x.pt              2

Visualize your experiments via Neptune.AI:

$ yolov5 train --data data.yaml --weights yolov5s.pt --neptune_project NAMESPACE/PROJECT_NAME --neptune_token YOUR_NEPTUNE_TOKEN
Inference

yolov5 detect command runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect.

$ yolov5 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
Export

You can export your fine-tuned YOLOv5 weights to any format such as torchscript, onnx, coreml, pb, tflite, tfjs:

$ yolov5 export --weights yolov5s.pt --include 'torchscript,onnx,coreml,pb,tfjs'

Keywords

FAQs


Did you know?

Socket

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts

SocketSocket SOC 2 Logo

Product

  • Package Alerts
  • Integrations
  • Docs
  • Pricing
  • FAQ
  • Roadmap
  • Changelog

Packages

npm

Stay in touch

Get open source security insights delivered straight into your inbox.


  • Terms
  • Privacy
  • Security

Made with ⚡️ by Socket Inc