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README.md

Forced Spatial Attention for Driver Foot Activity Classification

PyTorch implementation for the training procedure described in Forced Spatial Attention for Driver Foot Activity Classification.

Installation

  1. Clone this repository
  2. Install Pipenv:
pip3 install pipenv
  1. Install all requirements and dependencies in a new virtual environment using Pipenv:
cd Forced-Spatial-Attention
pipenv install
  1. Get link for desired PyTorch and Torchvision wheel from here and install it in the Pipenv virtual environment as follows:
pipenv install https://download.pytorch.org/whl/cu100/torch-1.2.0-cp36-cp36m-manylinux1_x86_64.whl
pipenv install https://download.pytorch.org/whl/cu100/torchvision-0.3.0-cp36-cp36m-linux_x86_64.whl

Dataset

  1. Download the trainval dataset for driver foot activity classification using this link.
  2. Extract the data.

Training

The prescribed two-stage training procedure for the classification network can be carried out as follows:

pipenv shell # activate virtual environment
python train_stage1.py --dataset-root-path=/path/to/dataset/ --snapshot=./weights/squeezenet1_1_imagenet.pth --version=1_1 --FSA
python train_stage2.py --dataset-root-path=/path/to/dataset/ --snapshot=/path/to/snapshot/from/stage1/training --version=1_1 --FSA
exit # exit virtual environment

Inference

Pretrained weights for SqueezeNet v1.1 using the two-stage FSA loss can be found here. Inference can be carried out using this script as follows:

pipenv shell # activate virtual environment
python demo.py --video=/path/to/dataset/foot.mp4 --snapshot=/path/to/snapshot --version=1_1
exit # exit virtual environment

Config files, logs, results and snapshots from running the above scripts will be stored in the Forced-Spatial-Attention /experiments folder by default.

About

Akshay Rangesh and Mohan Trivedi, "Forced Spatial Attention for Driver Foot Activity Classification," ICCV Workshop on Assistive Computer Vision and Robotics, 2019.

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