Skip to main content

YOLOv5 module for use with Autodistill

Project description

Autodistill YOLOv5 Module

This repository contains the code supporting the YOLOv5 target model for use with Autodistill.

YOLOv5 is an open-source computer vision model by Ultralytics, the creators of YOLOv5. You can use autodistill to train a YOLOv5 object detection model on a dataset of labelled images generated by the base models that autodistill supports.

View our YOLOv5 Instance Segmentation page for information on how to train instance segmentation models.

Read the full Autodistill documentation.

Read the YOLOv5 Autodistill documentation.

Installation

To use the YOLOv5 target model, you will need to install the following dependency:

pip3 install autodistill-yolov5

Quickstart

from autodistill_YOLOv5 import YOLOv5

target_model = YOLOv5("YOLOv5n.pt")

# train a model
target_model.train("./context_images_labeled/data.yaml", epochs=200)

# run inference on the new model
pred = target_model.predict("./context_images_labeled/train/images/dog-7.jpg", conf=0.01)

License

The code in this repository is licensed under an AGPL 3.0 license.

🏆 Contributing

We love your input! Please see the core Autodistill contributing guide to get started. Thank you 🙏 to all our contributors!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

autodistill-yolov5-0.1.2.tar.gz (14.9 kB view details)

Uploaded Source

Built Distribution

autodistill_yolov5-0.1.2-py3-none-any.whl (15.4 kB view details)

Uploaded Python 3

File details

Details for the file autodistill-yolov5-0.1.2.tar.gz.

File metadata

  • Download URL: autodistill-yolov5-0.1.2.tar.gz
  • Upload date:
  • Size: 14.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.6

File hashes

Hashes for autodistill-yolov5-0.1.2.tar.gz
Algorithm Hash digest
SHA256 f2d62ce80225456a62c15c87863c70bb394f7b9fa16d3693c21f4609534be0b7
MD5 25925a73bf59437b56916df49451e075
BLAKE2b-256 0a8e87293c5eb4b71a4c1026f1db5e404867b147cc5c1de7bcabeb1dd093c63b

See more details on using hashes here.

File details

Details for the file autodistill_yolov5-0.1.2-py3-none-any.whl.

File metadata

File hashes

Hashes for autodistill_yolov5-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 c6cbd2ce26aeef3642e7619ccf03174c7ac782c49aa60e1060d64b7f7b2219ce
MD5 2e48441004fbd156d5006b3e4d2cd816
BLAKE2b-256 7095686accf72e93c0b0af376824ecf818fc97d77d3934830f1458e034c0386e

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page