Effcient-CLS

This repository is the official implementation of the following paper:

Label-Efficient Online Continual Object Detection in Streaming Video
Jay Zhangjie Wu, David Junhao Zhang, Wynne Hsu, Mengmi Zhang, Mike Zheng Shou

Setup

Installation

Clone the repository and install the dependencies:

git clone https://github.com/showlab/Efficient-CLS.git
pip install Efficient-CLS/requirements.txt
python -m pip install -e Efficient-CLS

Datasets

We provide the processed datasets in the Google Drive (OAK, EgoObjects). Download the datasets and modify the DATA_DIR in configs/efficient_cls.yaml to the corresponding directory.

Pretrained Models

We use Faster R-CNN on PASCAL VOC object detection. Run the following commands to download the pretrained weights in Detectron2 Model Zoo.

mkdir weights && wget https://dl.fbaipublicfiles.com/detectron2/PascalVOC-Detection/faster_rcnn_R_50_C4/142202221/model_final_b1acc2.pkl -P weights/

Usage

To start training, run this:

# E.g., run experiment on OAK dataset at 4/16 annotation cost, with 12/16 unlabeled data trained with pseudo labels.
python train.py --exp=train --dataset=oak --num_oracle=4 --num_pseudo=12 --replay_size=16

Shoutouts

• This code builds on detectron2. Thanks for opensourcing!
• Thanks the contributors of OAK and EgoObjects for sharing the datasets!