X-ray specs for neural networks, circa 2016

What it does

Feeds an image through VGG16, then overlays a heatmap of the class-specific gradients onto the original image. The hotter the pixel, the more it influenced the model’s decision. It’s a diagnostic tool: is your network looking at the cat’s face, or the decorative vase behind it?

The interesting bit

The original implementation was in Torch; this is a clean Keras/TensorFlow rewrite from the early days of mainstream deep learning. The “guided” variant (shown in examples) combines Grad-CAM with guided backprop for sharper visualizations — think of it as Grad-CAM with the blur filter turned off.

Key highlights

• Single-file implementation: grad-cam.py and a command-line invocation
• Defaults to VGG16 from keras.applications (weights auto-downloaded)
• Includes both vanilla Grad-CAM and Guided Grad-CAM outputs
• Paper-correct: links directly to Selvaraju et al.’s 2016 arXiv preprint
• Example gallery covers boats, persian cats, and the classic cat-dog ambiguity set

Caveats

• Hard-coded to TensorFlow dimension ordering; Theano users need to adapt
• README doesn’t specify Keras/TensorFlow version compatibility — this is 2016-era code, so modern tf.keras may need tweaks
• No API or library packaging; it’s a script you copy and modify

Verdict

Worth a look if you’re teaching CNN interpretability or debugging a legacy Keras model. Skip it if you need a maintained, pip-installable tool — the ecosystem has moved on to packages like tf-keras-vis.