Metadata-Version: 2.1
Name: torchcam
Version: 0.1.2
Summary: Class activation maps for your PyTorch CNN models
Home-page: https://github.com/frgfm/torch-cam
Author: François-Guillaume Fernandez
License: MIT
Download-URL: https://github.com/frgfm/torch-cam/tags
Description: 
        # Torchcam: class activation explorer
        
        [![License](https://img.shields.io/badge/License-MIT-brightgreen.svg)](LICENSE) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/5124b1dff75e4e9cbb68136516605032)](https://www.codacy.com/manual/frgfm/torch-cam?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=frgfm/torch-cam&amp;utm_campaign=Badge_Grade)  ![Build Status](https://github.com/frgfm/torch-cam/workflows/python-package/badge.svg) [![codecov](https://codecov.io/gh/frgfm/torch-cam/branch/master/graph/badge.svg)](https://codecov.io/gh/frgfm/torch-cam) [![Docs](https://img.shields.io/badge/docs-available-blue.svg)](https://frgfm.github.io/torch-cam)  [![Pypi](https://img.shields.io/badge/pypi-v0.1.1-blue.svg)](https://pypi.org/project/torchcam/) 
        
        Simple way to leverage the class-specific activation of convolutional layers in PyTorch.
        
        ![gradcam_sample](static/images/cam_example.png)
        
        
        
        ## Table of Contents
        
        * [Getting Started](#getting-started)
          * [Prerequisites](#prerequisites)
          * [Installation](#installation)
        * [Usage](#usage)
        * [Documentation](#documentation)
        * [Contributing](#contributing)
        * [Credits](#credits)
        * [License](#license)
        
        
        
        ## Getting started
        
        ### Prerequisites
        
        - Python 3.6 (or more recent)
        - [pip](https://pip.pypa.io/en/stable/)
        
        ### Installation
        
        You can install the package using [pypi](https://pypi.org/project/torch-cam/) as follows:
        
        ```shell
        pip install torchcam
        ```
        
        or using [conda](https://anaconda.org/frgfm/torchcam):
        
        ```shell
        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.
        
        ```shell
        python scripts/cam_example.py --model resnet50 --class-idx 232
        ```
        
        ![gradcam_sample](static/images/cam_example.png)
        
        
        
        ## Documentation
        
        The full package documentation is available [here](https://frgfm.github.io/torch-cam/) for detailed specifications. The documentation was built with [Sphinx](sphinx-doc.org) using a [theme](github.com/readthedocs/sphinx_rtd_theme) provided by [Read the Docs](readthedocs.org).
        
        
        
        ## 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](https://arxiv.org/abs/1512.04150): the original CAM paper
        - [Grad-CAM](https://arxiv.org/abs/1610.02391): GradCAM paper, generalizing CAM to models without global average pooling. 
        - [Grad-CAM++](https://arxiv.org/abs/1710.11063): improvement of GradCAM++ for more accurate pixel-level contribution to the activation.
        - [Smooth Grad-CAM++](https://arxiv.org/abs/1908.01224): SmoothGrad mechanism coupled with GradCAM.
        - [Score-CAM](https://arxiv.org/abs/1910.01279): score-weighting of class activation for better interpretability.
        - [SS-CAM](https://arxiv.org/abs/2006.14255): SmoothGrad mechanism coupled with Score-CAM.
        - [IS-CAM](https://arxiv.org/abs/2010.03023): integration-based variant of Score-CAM.
        
        
        
        ## License
        
        Distributed under the MIT License. See `LICENSE` for more information.
Keywords: pytorch,deep learning,cnn,convolution,activation,gradcam
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
