Skip to content

Latest commit

 

History

History
43 lines (28 loc) · 1.72 KB

README.md

File metadata and controls

43 lines (28 loc) · 1.72 KB
Raw

Keras implementation of class activation mapping

Paper / project page: http://cnnlocalization.csail.mit.edu

Paper authors' code with Caffe / matcaffe interface: https://github.com/metalbubble/CAM

Blog post on this repository: http://jacobcv.blogspot.com/2016/08/class-activation-maps-in-keras.html

Checkpoint with person/not person weights: https://drive.google.com/open?id=0B1l5JSkBbENBdk95ZW1DOUhqQUE

enter image description here

This project implements class activation maps with Keras.

Class activation maps are a simple technique to get the image regions relevant to a certain class.

This was fined tuned on VGG16 with images from here: http://pascal.inrialpes.fr/data/human

The model in model.py is a two category classifier, used to classify person / not a person.

python cam.py --model_path cam_checkpoint.hdf5 --image_path=image.jpg

usage: cam.py [-h] [--train TRAIN] [--image_path IMAGE_PATH]
          [--output_path OUTPUT_PATH] [--model_path MODEL_PATH]
          [--dataset_path DATASET_PATH]

optional arguments:
  -h, --help            show this help message and exit
  --train TRAIN         Train the network or visualize a CAM
  --image_path IMAGE_PATH
                        Path of an image to run the network on
  --output_path OUTPUT_PATH
                        Path of an image to run the network on
  --model_path MODEL_PATH
                        Path of the trained model
  --dataset_path DATASET_PATH
                        Path to image dataset. Should have pos/neg folders,
                        like in the inria person dataset.
                        http://pascal.inrialpes.fr/data/human/