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BoB-OOD-Detection

This repository is the official implementation of <strong>OOD Object Detection</strong> task in the Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks.

:pushpin: Our implementation and instructions are based on mmdetection

Installation

Step 1. Create a conda environment and activate it.

conda create --name openmmlab python=3.8 -y
conda activate openmmlab

Step 2. Install PyTorch following official instructions, e.g.

On GPU platforms:

conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia

Step 3. Install MMCV using MIM.

pip install -U openmim
mim install mmcv-full==1.7.0

Step 4. Install BoB-OOD-Detection.

git clone https://github.com/hsouri/bob-ood-detection.git
cd bob-ood-detection
pip install -v -e .
# "-v" means verbose, or more output
# "-e" means installing a project in editable mode,
# thus any local modifications made to the code will take effect without reinstallation.

Step 5. Setup Datasets.

Download the <a href="https://fcav.engin.umich.edu/projects/driving-in-the-matrix">Sim10k</a> dataset and run the following command to process annotations.

python dataset_utils/sim10k_voc2coco_format.py \
    --sim10k_path <path-to-sim10k-folder> \
    --img-dir <path-to-sim10k-images> \
    --gt-dir <path-to-sim10k-annotations> \
    --out-dir <path-to-store-processed-annotations>

Download the <a href="https://www.cityscapes-dataset.com/downloads/">Cityscapes</a> dataset.

Once processed, update the path to individual datasets in the experiment configs at configs/bob_sim2real.

If required, please refer to Get Started, Dataset Prepare, and Dataset Download for more detailed instructions.

Usage

The config files for all experiments in <strong>Battle of the Backbones (BoB)</strong> can be found configs/bob_sim2real.

To train a detector with the existing configs, run:

bash ./tools/dist_train.sh <CONFIG_FILE> <GPU_NUM>