This project is based on facebookresearch's maskrcnn-benchmark.
conda create --name py36 python=3.6
conda activate py36
conda install -c anaconda numpy
conda install -c anaconda cython
conda install ipython
pip install tqdm yacs
mkdir ~/github
cd ~/github
export INSTALL_DIR=$PWD
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install
cd ~/github
git clone https://github.com/xanderchf/faster-rcnn-KITTI-BDD100k.git
cd faster-rcnn-KITTI-BDD100k
conda install -c pytorch pytorch torchvision cudatoolkit=9.2
python setup.py build develop
unset INSTALL_DIR
pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.12.0-cp36-cp36m-linux_x86_64.whl
pip install tensorboardX
This repo assumes that you followed NVIDIA DIGIT's instruction to prepare the KITTI dataset.
Prepare the dataset with the below structure:
bdd100k/
images/
100k/
train/
val/
test/
labels/
bdd100k_labels_images_train.json
bdd100k_labels_images_val.json
Finally, create a symlink for the datasets:
ln -s {parent directory of kitti and bdd100k} {root directory of this repo}/datasets
python tools/train_net.py --config-file "configs/e2e_faster_rcnn_R_101_FPN_1x.yaml" --use_tensorboard MODEL.ROI_BOX_HEAD.NUM_CLASSES 7 DATASETS.TRAIN '("kitti_train",)' OUTPUT_DIR out_kitti
python tools/train_net.py --config-file "configs/e2e_faster_rcnn_R_101_FPN_1x.yaml" --use_tensorboard MODEL.ROI_BOX_HEAD.NUM_CLASSES 11 DATASETS.TRAIN '("bdd100k_train",)' OUTPUT_DIR out_bdd
For the BDD100K dataset, you might need to clip the gradient for training stability. For other hyperparameters, refer to maskrcnn-benchmark.
Below commands loads saved weights and save the detections in the BDD format in {OUTPUT_DIR}/detections.
python tools/test_net.py --weights out_kitti/*.pth --config-file "configs/e2e_faster_rcnn_R_101_FPN_1x.yaml" DATASETS.TEST '("kitti_val",)' OUTPUT_DIR out_kitti
python tools/test_net.py --weights out_bdd/*.pth --config-file "configs/e2e_faster_rcnn_R_101_FPN_1x.yaml" DATASETS.TEST '("bdd100k_val",)' OUTPUT_DIR out_bdd
To evaluate the saved detection, run the below command:
python tools/evaluate.py --result out_bdd/detections DATASETS.TEST '("bdd100k_val",)'
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