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BoB-Detection

This repository is the official implementation of <strong>Object Detection and Instance Segmentation</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-Detection.

git clone https://github.com/hsouri/bob-detection.git
cd bob-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. Download COCO (LVIS) and unzip dataset (you can optionally delete downloaded zip files by passing '--delete').

COCO download:

python tools/misc/download_dataset.py --dataset-name coco2017 --unzip

LVIS download:

python tools/misc/download_dataset.py --dataset-name lvis --save-dir data/lvis_v1/ --unzip
cd data/lvis_v1/
mkdir annotations
mv lvis_v1_train.json annotations/
mv lvis_v1_val.json annotations/

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.

To train a detector with the existing configs, run:

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