alokwhitewolf / Class-Activation-Mappings

Visualizing where the Convolution Network is looking through CAM.

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Class-Activation-Mappings

Chainer implementation of Learning Deep Features for Discriminative Localization

This implementation uses ResNet50 architecture to infer the class activations. Image

Class activation maps are a simple technique to get the discriminative image regions used by a CNN to identify a specific class in the image. In other words, a class activation map (CAM) lets us see which regions in the image were relevant to this class. The authors of the paper show that this also allows re-using classifiers for getting good localization results, even when training without bounding box coordinates data. This also shows how deep learning networks already have some kind of a built in attention mechanism.This should be useful for debugging the decision process in classification networks.

Image

Dependencies

To run

Trained Chainer model for ResNet50 is stored in model. To test,

python main.py -i <path-to-image>

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Visualizing where the Convolution Network is looking through CAM.

License:MIT License


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Language:Python 100.0%