torchcam 0.1.2

Class activation maps for your PyTorch CNN models

Torchcam: class activation explorer

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

Table of Contents

• Getting Started
  • Prerequisites
  • Installation
• Usage
• Documentation
• Contributing
• Credits
• License

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

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:

• Learning Deep Features for Discriminative Localization: the original CAM paper
• Grad-CAM: GradCAM paper, generalizing CAM to models without global average pooling.
• Grad-CAM++: improvement of GradCAM++ for more accurate pixel-level contribution to the activation.
• Smooth Grad-CAM++: SmoothGrad mechanism coupled with GradCAM.
• Score-CAM: score-weighting of class activation for better interpretability.
• SS-CAM: SmoothGrad mechanism coupled with Score-CAM.
• IS-CAM: integration-based variant of Score-CAM.

License

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