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DetGPT: Detect What You Need via Reasoning

News

[2023-06-13] Added tuned linear weights for Vicuna-7b.
[2023-05-25] Our paper is available at this link.
[2023-05-09] We have launched our project website.
[2023-05-08] The first version of DetGPT is available now! Try our demo.

Online Demo

Due to high website traffic, we have created multiple online services. If one link is not working, please use another one. Thank you for your support!

Demo

演示 (简体中文)

Demo (backup)

Examples

Features

DetGPT locates target objects, not just describing images.
DetGPT understands complex instructions, like "Find blood pressure-reducing foods in the image."
DetGPT accurately localizes target objects via LLM reasoning. - For example, it can identify bananas as a potassium-rich food to alleviate high blood pressure.
DetGPT provides answers beyond human common sense, like identifying unfamiliar fruits rich in potassium.

Setup

1. Installation

git clone https://github.com/OptimalScale/DetGPT.git
cd DetGPT
conda create -n detgpt python=3.9 -y
conda activate detgpt
pip install -e .

2. Install GroundingDino

python -m pip install -e GroundingDINO

2. Download the pretrained checkpoint and task tuning dataset

Our model is based on pretrained language model checkpoints. In our experiments, we use Robin from LMFlow team, and Vicuna and find they perform competitively well. You can run following script to download the Robin checkpoint:

cd output_models
bash download.sh all
cd -

Merge the robin lora model with the original llama model and save the merged model to output_models/robin-7b, where the corresponding model path is specified in this config file here.

To obtain the original llama model, one may refer to this doc. To merge a lora model with a base model, one may refer to PEFT or use the merge script provided by LMFlow.

The dataset for task tuning is named "coco_task_annotation.json". Please modify detgpt/configs/datasts/coco/align.yaml, such that "storage" points to the COCO dataset, and "file_name" points to the path of the instruction tuning dataset.

Data Preparation

cd dataset
mkdir coco

Download the COCO dataset from COCO home page.

Here is the data structure:

dataset/coco/
├── train2017/
├── val2017/
├── annotations.json
├── coco_task_annotation.json

Note: Please move coco_task_annotation.json from output_models/ to coco/

Training

Please execute the following command to conduction task training:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --nproc-per-node 8 train.py --cfg-path path-to-config

Note that we provide two example config files for task tuning under configs/ directory. You need to replace model/ckpt with the path to pretrained linear weights of first stage.

Deploy Demo Locally

Run the demo by executing the following command. Replace 'path/to/pretrained_linear_weights' in the config file to the real path. We currently release linear weights based on Vicuna-13B-v1.1 and will release other weights later. The demo runs on 2 GPUs by default, one for the language model and another for GroundingDino.

CUDA_VISIBLE_DEVICES=0,1 python demo_detgpt.py --cfg-path configs/detgpt_eval_13b.yaml

Acknowledgement

The project is built on top of the amazing open-vocabulary detector GroundingDino and multimodal conversation model MiniGPT-4, which is based on BLIP2 and Lavis. Thanks for these great work!

If you're using DetGPT in your research or applications, please cite using this BibTeX:

@misc{pi2023detgpt,
      title={DetGPT: Detect What You Need via Reasoning}, 
      author={Renjie Pi and Jiahui Gao and Shizhe Diao and Rui Pan and Hanze Dong and Jipeng Zhang and Lewei Yao and Jianhua Han and Hang Xu and Lingpeng Kong and Tong Zhang},
      year={2023},
      eprint={2305.14167},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

License

This repository is released under BSD 3-Clause License.