Awesome
Explainable Object-induced Action Decision for Autonomous Vehicles
The repo for our cvpr 2020 paper. We used maskrcnn benchmark for bounding box extraction. The project page is also available here.
<p align="center"> <img src="./images/net.png" alt="net" width="900"> <p align="center"> <em>Proposed architecture.</em> </p> </p>Installation
Step 1
Clone this repo.
git clone https://github.com/Twizwei/bddoia_project
Step 2
Install maskrcnn benchmark. Follow the instructions here to install maskrcnn benchmark.
Dataset
Download the our dataset BDD-OIA and then extract it.
To run our model for last frames, it is better to establish symbolic link.
cd ./maskrcnn/maskrcnn-benchmark
mkdir datasets
ln -s dir_to_lastframe datasets
BDD-OIA also contains data for videos.
Training and evaluation
First we need to train Faster RCNN on BDD100K. The pretrained Faster RCNN is available here
To train the model, run
python ./maskrcnn/maskrcnn-benchmark/action_prediction/train.py --batch size 2 --num_epoch 50 --initLR 0.001 --gtroot "root-to-action-gt" --reasonroot "root-to-explanation-gt" MODEL.SIDE True MODEL.ROI_HEADS.SCORE_THRESH 0.4 MODEL.PREDICTOR_NUM 1 OUTPUT_DIR "output-directory" MODEL.META_ARCHITECTURE "Baseline1"
Training configurations can be found in train.py and
maskrcnn/maskrcnn-benchmark/maskrcnn_benchmark/config/defaults/py .
To evaluate the model, run
python ./maskrcnn/maskrcnn-benchmark/action_prediction/test.py --batch size 2 --gtroot "root-to-action-gt" --reasonroot "root-to-explanation-gt" WEIGHT "weights-dir" MODEL.SIDE True MODEL.ROI_HEADS.SCORE_THRESH 0.4 MODEL.PREDICTOR_NUM 1 OUTPUT_DIR "output-directory" MODEL.META_ARCHITECTURE "Baseline1"
A pretrained model net_Final.pth is also available here.