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Class activation maps for your PyTorch CNN models

Project description

Torchcam: class activation explorer

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Simple way to leverage the class-specific activation of convolutional layers in PyTorch.

gradcam_sample

Table of Contents

Getting started

Prerequisites

  • Python 3.6 (or more recent)
  • pip

Installation

You can install the package using pypi as follows:

pip install torchcam

or using conda:

conda install -c frgfm torchcam

Usage

You can find a detailed example below to retrieve the CAM of a specific class on a resnet architecture.

python scripts/cam_example.py --model resnet50 --class-idx 232

gradcam_sample

Documentation

The full package documentation is available here for detailed specifications. The documentation was built with Sphinx using a theme provided by Read the Docs.

Contributing

Please refer to CONTRIBUTING if you wish to contribute to this project.

Credits

This project is developed and maintained by the repo owner, but the implementation was based on the following precious papers:

License

Distributed under the MIT License. See LICENSE for more information.

Project details


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