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Detect Every Thing with Few Examples

Arxiv Paper

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Update: This paper is resubmitted from ICLR2024 to another conference. I improved this work's presentation in the new draft and simplified the implementation. I will update the code later.

We present DE-ViT, an open-set object detector in this repository. In contrast to the popular open-vocabulary approach, we follow the few-shot formulation to represent each category with few support images rather than language. Our results shows potential for using images as category representation. DE-ViT establishes new state-of-the-art on open-vocabulary, few-shot, and one-shot object detection benchmark with COCO and LVIS.

Installation

git clone https://github.com/mlzxy/devit.git
conda create -n devit  python=3.9 
conda activate devit
pip install -r devit/requirements.txt
pip install -e ./devit

Next, check Downloads.md for instructions to setup datasets and model checkpoints.

Running Scripts

Download datasets and checkpoints before running scripts.

Demo

python3 ./demo/demo.py # will generate demo/output/ycb.out.jpg

The notebook demo/build_prototypes.ipynb builds prototypes for YCB objects using ViT-L/14 and our provided example images.

Training

vit=l task=ovd dataset=coco bash scripts/train.sh  # train open-vocabulary COCO with ViT-L

# task=ovd / fsod / osod
# dataset=coco / lvis
# vit=s / b / l

# few-shot env var `shot = 5 / 10 / 30`
vit=l task=fsod shot=10 bash scripts/train.sh 

# one-shot env var `split = 1 / 2 / 3 / 4`
vit=l task=osod split=1 bash script/train.sh

# detectron2 options can be provided through args, e.g.,
task=ovd dataset=lvis bash scripts/train.sh MODEL.MASK_ON True # train lvis with mask head

# another env var is `num_gpus = 1 / 2 ...`, used to control
# how many gpus are used

Evaluation

All evaluations can be run without training, as long as the checkpoints are downloaded.

The script-level environment variables are the same to training.

vit=l task=ovd dataset=coco bash scripts/eval.sh # evaluate COCO OVD with ViT-L/14

vit=l task=ovd dataset=lvis bash scripts/eval.sh DE.TOPK 3  MODEL.MASK_ON True  # evaluate LVIS OVD with ViT-L/14

RPN Training (COCO)

bash scripts/train_rpn.sh  ARG
# change ARG to ovd / os1 / os2 / os3 / os4 / fs14
# corresponds to open-vocabulary / one-shot splits 1-4 / few-shot

Check Tools.md for intructions to build prototype and prepare weights.

Acknowledgement

This repository was built on top of RegionCLIP and DINOv2. We thank the effort from our community.

Citation

@misc{zhang2023detect,
      title={Detect Every Thing with Few Examples}, 
      author={Xinyu Zhang and Yuting Wang and Abdeslam Boularias},
      year={2023},
      eprint={2309.12969},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}