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ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection

This is an implementation of ADA using Python, Pytorch and Matlab.

The repository includes:

  • Library of edge_boxes and faster_rcnn for bounding box proposal and feature extracting.
  • Python code of the ADA algorithm.
  • Part results of experiment on VOC 2007 dataset.

Getting Started

1. Download gurobi and activate it.

2. Compile faster_rcnn

Steps

1. Preprocess steps to extract features:

obtain and store bounding box proposals information.

run get_box_proposals.py.

obtain and store features of proposals and groundtruths.

run feature_extractor_bbslist.py and feature_extractor_gt.py.

2. Train

run train_ada.py.

3. Test

run test_ada.py.

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