Bottom-Up Features Extractor

This code implements an extraction of Bottom-up image features (paper). Based on the original bottom-up attention model and PyTorch implementation of Faster R-CNN.

Requirements

• Python 3.6
• PyTorch 0.4.0
• CUDA 9.0

Note: CPU version is not supported.

Installation

1. Clone the code:

   git clone https://github.com/violetteshev/bottom-up-features.git
   

2. Install PyTorch with pip:

   pip install https://download.pytorch.org/whl/cu90/torch-0.4.0-cp36-cp36m-linux_x86_64.whl
   

   or with Anaconda:

   conda install pytorch=0.4.0 cuda90 -c pytorch
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Compile the code:

   cd lib
   sh make.sh
   

5. Download the pretrained model from dropbox or google drive and put it in models/ folder.

Feature Extraction

1. To extract image features and store them in .npy format:

   python extract_features.py --image_dir images --out_dir features
   

2. To save bounding boxes use --boxes argument:

   python extract_features.py --image_dir images --out_dir features --boxes