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:rainbow: :camera: Gradient-weighted Class Activation Mapping (Grad-CAM) Demo

Home Page: http://gradcam.cloudcv.org/

Python 12.84% Lua 8.85% Shell 0.57% CSS 0.08% HTML 50.23% JavaScript 27.43%
grad-cam cnn demo deep-learning machine-learning torch

grad-cam's Introduction

Grad-CAM: Gradient-weighted Class Activation Mapping

Join the chat at https://gitter.im/Cloud-CV/Grad-CAM

Grad-CAM uses the class-specific gradient information flowing into the final convolutional layer of a CNN to produce a coarse localization map of the important regions in the image. It is a novel technique for making CNN more 'transparent' by producing visual explanations i.e visualizations showing what evidence in the image supports a prediction. You can play with Grad-CAM demonstrations at the following links:

Arxiv Paper Link: https://arxiv.org/abs/1610.02391

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Grad-CAM Classification Demo: http://gradcam.cloudcv.org/classification

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Grad-CAM Captioning Demo: http://gradcam.cloudcv.org/captioning

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Installing / Getting started

We use RabbitMQ to queue the submitted jobs. Also, we use Redis as backend for realtime communication using websockets.

All the instructions for setting Grad-CAM from scratch can be found here

Note: For best results, its recommended to run the Grad-CAM demo on GPU enabled machines.

Interested in Contributing?

Cloud-CV always welcomes new contributors to learn the new cutting edge technologies. If you'd like to contribute, please fork the repository and use a feature branch. Pull requests are warmly welcome.

if you have more questions about the project, then you can talk to us on our Gitter Channel.

Acknowledgements

grad-cam's People

Contributors

abhshkdz avatar deshraj avatar ramprasaath avatar ramprs avatar rishabhjain2018 avatar

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grad-cam's Issues

Change directory to save results

Currently, the resultant images are stored in the demo images folder and hence causing the problem of showing the result images as demo images.

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