bhanukaManesha/faster-rcnn.pytorch

This repository was cloned from an earlier version of faster-rcnn.pytorch repository.

It contains scripts to extract FasterRCNN features for CLEVR dataset.

You can download extracted features from here.

It has two keys:

1. image_features of shape N x num_objects x feature_dims, contains the image features of 15 objects per image of 2048 dimensions.
2. box_features of shape N x num_objects x 6, where the 6 dimensions refer to: (x1, y1, x2, y2, width, height). They are normalized to 0 and 1.

To extract features from CLEVR dataset

1. Compile the library by executing make.sh inside the lib directory. Note that I had faced several issues while compiling the library. I used the following setup/modifications, which may be helpful to you too:

   a. It requires Pytorch version: 0.4.0 (Versions 0.4.1 and 1.0 do not work!). You can install the correct dependencies using:

   conda install pytorch=0.4.0 torchvision -c pytorch

   b. You may have to edit the CUDA_ARCH variable inside lib/make.sh to ensure things are compatible with your GPU.

2. Download pre-trained FasterRCNN model to a path, say, to: ${ROOT}/FasterRCNN/models/res101/clevr This model has been trained on training images of CLEVR dataset.

3. Download objects_count.json inside ${ROOT}/CLEVR/faster-rcnn/

4. Put CLEVR images inside the following directories:

   a. Train images inside ${ROOT}/CLEVR/images/train

   b. Val images inside ${ROOT}/CLEVR/images/val

   c. Test images inside ${ROOT}/CLEVR/images/test

5. Execute ./extract_resnet_features_CLEVR.sh This will extract the features to ${ROOT}/CLEVR/features

Here is the link to the original repository.