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

Note: CPU version is not supported.

Clone the code:

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

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

Install dependencies:
```
pip install -r requirements.txt
```
Compile the code:
```
cd lib
sh make.sh
```
Download the pretrained model from dropbox or google drive and put it in models/ folder.

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

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

To save bounding boxes use --boxes argument:

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