🦖 OV-DINO

Unified Open-Vocabulary Detection with Language-Aware Selective Fusion

Hao Wang^1,2,Pengzhen Ren¹,Zequn Jie³, Xiao Dong¹, Chengjian Feng³, Yinlong Qian³,

Lin Ma³, Dongmei Jiang², Yaowei Wang^2,4, Xiangyuan Lan²^📧, Xiaodan Liang^1,2^📧

¹ Sun Yat-sen University, ² Pengcheng Lab, ³ Meituan Inc, ⁴ HIT, Shenzhen

^📧 corresponding author.

[Paper] [HuggingFace] [Demo] [BibTex]

🔥 Updates

15/08/2024: ✨ Have a look!!! We release the pre-training code and log on O365 dataset. You could try to reproduce our results.
06/08/2024: 🎇 Awesome!!! OV-SAM = OV-DINO + SAM2. We update OV-SAM marrying OV-DINO with SAM2 on the online demo.
16/07/2024: We provide the online demo, click and enjoy !!! NOTE: You uploaded image will be stored for failure analysis.
16/07/2024: We release the web inference demo, try to deploy it by yourself.
15/07/2024: We release the fine-tuning code, try to fine-tune on your custom dataset. Feel free to raise issue if you encounter some problem.
15/07/2024: We release the local inference demo, try to deploy OV-DINO on you local machine and run inference on images.
14/07/2024: We release the pre-trained models and the evaluation code.
11/07/2024: We release OV-DINO paper on arxiv. Code and pre-trained model are coming soon.

🚀 Introduction

This project contains the official PyTorch implementation, pre-trained models, fine-tuning code, and inference demo for OV-DINO.

OV-DINO is a novel unified open vocabulary detection approach that offers superior performance and effectiveness for practical real-world application.
OV-DINO entails a Unified Data Integration pipeline that integrates diverse data sources for end-to-end pre-training, and a Language-Aware Selective Fusion module to improve the vision-language understanding of the model.
OV-DINO shows significant performance improvement on COCO and LVIS benchmarks compared to previous methods, achieving relative improvements of +2.5% AP on COCO and +12.7% AP on LVIS compared to G-DINO in zero-shot evaluation.

📄 Overview

✨ Model Zoo

Model	Pre-Train Data	AP^mv	AP_r	AP_c	AP_f	AP^val	AP_r	AP_c	AP_f	AP^coco	Weights
OV-DINO¹	O365	24.4	15.5	20.3	29.7	18.7	9.3	14.5	27.4	49.5 / 57.5	CKPT / LOG 🤗
OV-DINO²	O365,GoldG	39.4	32.0	38.7	41.3	32.2	26.2	30.1	37.3	50.6 / 58.4	CKPT 🤗
OV-DINO³	O365,GoldG,CC1M^‡	40.1	34.5	39.5	41.5	32.9	29.1	30.4	37.4	50.2 / 58.2	CKPT 🤗

NOTE: AP^mv denotes the zero-shot evaluation results on LVIS MiniVal, AP^val denotes the zero-shot evaluation results on LVIS Val, AP^coco denotes (zero-shot / fine-tune) evaluation results on COCO, respectively.

🏁 Getting Started

1. Project Structure

OV-DINO
├── datas
│   ├── o365
│   │   ├── annotations
│   │   ├── train
│   │   ├── val
│   │   └── test
│   ├── coco
│   │   ├── annotations
│   │   ├── train2017
│   │   └── val2017
│   ├── lvis
│   │   ├── annotations
│   │   ├── train2017
│   │   └── val2017
│   └── custom
│       ├── annotations
│       ├── train
│       └── val
├── docs
├── inits
│   ├── huggingface
│   ├── ovdino
│   ├── sam2
│   └── swin
├── ovdino
│   ├── configs
│   ├── demo
│   ├── detectron2-717ab9
│   ├── detrex
│   ├── projects
│   ├── scripts
│   └── tools
├── wkdrs
│   ├── ...
│

2. Installation

# clone this project
git clone https://github.com/wanghao9610/OV-DINO.git
cd OV-DINO
export root_dir=$(realpath ./)
cd $root_dir/ovdino

# Optional: set CUDA_HOME for cuda11.6.
# OV-DINO utilizes the cuda11.6 default, if your cuda is not cuda11.6, you need first export CUDA_HOME env manually.
export CUDA_HOME="your_cuda11.6_path"
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
echo -e "$log_format cuda version:\n$(nvcc -V)"

# create conda env for ov-dino
conda create -n ovdino -y
conda activate ovdino
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia -y
conda install gcc=9 gxx=9 -c conda-forge -y # Optional: install gcc9
python -m pip install -e detectron2-717ab9
pip install -e ./

# Optional: create conda env for ov-sam, it may not compatible with ov-dino, so we create a new env.
# ov-sam = ov-dino + sam2
conda create -n ovsam -y
conda activate ovsam
conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia -y
# install the sam2 following the sam2 project.
# please refer to https://github.com/facebookresearch/segment-anything-2.git
# download sam2 checkpoints and put them to inits/sam2
python -m pip install -e detectron2-717ab9
pip install -e ./

2. Data Preparing

COCO

Download COCO from the official website, and put them on datas/coco folder.

cd $root_dir
mkdir -p datas/coco
wget http://images.cocodataset.org/zips/train2017.zip -O datas/coco/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip -O datas/coco/val2017.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip -O datas/coco/annotations_trainval2017.zip

Extract the ziped files, and remove them:

cd $root_dir
unzip datas/coco/train2017.zip -d datas/coco
unzip datas/coco/val2017.zip -d datas/coco
unzip datas/coco/annotations_trainval2017.zip -d datas/coco
rm datas/coco/train2017.zip datas/coco/val2017.zip datas/coco/annotations_trainval2017.zip

LVIS

Download LVIS annotation files:

cd $root_dir
mkdir -p datas/lvis
wget https://huggingface.co/hao9610/OV-DINO/resolve/main/lvis_v1_minival_inserted_image_name.json -O datas/lvis/annotations/lvis_v1_minival_inserted_image_name.json
wget https://huggingface.co/hao9610/OV-DINO/resolve/main/lvis_v1_val_inserted_image_name.json -O datas/lvis/annotations/lvis_v1_val_inserted_image_name.json

Soft-link COCO to LVIS:

cd $root_dir
ln -s $(realpath datas/coco/train2017) datas/lvis
ln -s $(realpath datas/coco/val2017) datas/lvis

Objects365

Refer to the OpenDataLab for Objects365V1 download, which has provided detailed download instruction.

cd $root_dir
mkdir -p datas/o365/annotations
# Suppose you download the Objects365 raw file and put them on datas/o365/raw, extract the tared files and reorder them.
cd datas/o365/raw
tar -xvf Objects365_v1.tar.gz
cd 2019-08-02
for file in *.zip; do unzip -o "$file"; done
mv *.json $root_dir/datas/o365/annotations
mv train val test $root_dir/datas/o365

3. Evaluation

Download the pre-trained model from Model Zoo, and put them on inits/ovdino directory.

cd $root_dir/ovdino
bash scripts/eval.sh path_to_eval_config_file path_to_pretrained_model output_directory

Zero-Shot Evaluation on COCO Benchmark

cd $root_dir/ovdino
# Evaluation mean ap on COCO dataset.
bash scripts/eval.sh \
  projects/ovdino/configs/ovdino_swin_tiny224_bert_base_eval_coco.py \
  ../inits/ovdino/ovdino_swint_og-coco50.6_lvismv39.4_lvis32.2.pth \
  ../wkdrs/eval_ovdino

Zero-Shot Evaluation on LVIS Benchmark

cd $root_dir/ovdino
# Evaluation of fixed_ap on LVIS MiniVal dataset.
bash scripts/eval.sh \
  projects/ovdino/configs/ovdino_swin_tiny224_bert_base_eval_lvismv.py \
  ../inits/ovdino/ovdino_swint_ogc-coco50.2_lvismv40.1_lvis32.9.pth \
  ../wkdrs/eval_ovdino

# Evaluation of fixed_ap on the LVIS Val dataset. 
# It will require about 250GB of memory due to the large number of samples in the LVIS Val dataset, so please ensure that your machine has enough memory.
bash scripts/eval.sh \
  projects/ovdino/configs/ovdino_swin_tiny224_bert_base_eval_lvis.py \
  ../inits/ovdino/ovdino_swint_ogc-coco50.2_lvismv40.1_lvis32.9.pth \
  ../wkdrs/eval_ovdino

4. Fine-Tuning

Fine-Tuning on COCO Dataset

cd $root_dir/ovdino
bash scripts/finetune.sh \
  projects/ovdino/configs/ovdino_swin_tiny224_bert_base_ft_coco_24ep.py \
  ../inits/ovdino/ovdino_swint_og-coco50.6_lvismv39.4_lvis32.2.pth

Fine-Tuning on Custom Dataset

Prepare your custom dataset as the COCO annotation format, following the instructions on custom_ovd.py.

Refer the following command to run fine-tuning.

cd $root_dir/ovdino
bash scripts/finetune.sh \
  projects/ovdino/configs/ovdino_swin_tiny224_bert_base_ft_custom_24ep.py \
  ../inits/ovdino/ovdino_swint_ogc-coco50.2_lvismv40.1_lvis32.9.pth

5. Pre-Training

Pre-Training on Objects365 dataset

Download dataset following Objects365 Data Preparing.

Refer the following command to run pre-training.

On the first machine:

cd $root_dir/ovdino
# Replace $MASTER_PORT and $MASTER_ADDR with your actual machine settings.
NNODES=2 NODE_RANK=0 MASTER_PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR \
bash scripts/pretrain.sh \
  projects/ovdino/configs/ovdino_swin_tiny224_bert_base_pretrain_o365_24ep.py

On the second machine:

cd $root_dir/ovdino
# Replace $MASTER_PORT and $MASTER_ADDR with your actual machine settings.
NNODES=2 NODE_RANK=1 MASTER_PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR \
bash scripts/pretrain.sh \
  projects/ovdino/configs/ovdino_swin_tiny224_bert_base_pretrain_o365_24ep.py

NOTE: The default batch size for O365 pre-training is 64 in our experiments, and running on 2 nodes with 8 A100 GPUs per-node. If you encounter Out-of-Memory error, you can adjust the batch size and learning rate, total steps by linearly.

Pre-Training on [Objects365, GoldG] datasets

Coming soon ...

Pre-Training on [Objects365, GoldG, CC1M‡] datasets

Coming soon ...

NOTE: We will release the all pre-training code after our paper is accepted.

💻 Demo

Local inference on a image or folder give the category names.

# for ovdino: conda activate ovdino
# for ovsam: conda activate ovsam
cd $root_dir/ovdino
bash scripts/demo.sh demo_config.py pretrained_model category_names input_images_or_directory output_directory

Examples:

cd $root_dir/ovdino
# single image inference
bash scripts/demo.sh \
  projects/ovdino/configs/ovdino_swin_tiny224_bert_base_infer_demo.py \
  ../inits/ovdino/ovdino_swint_ogc-coco50.2_lvismv40.1_lvis32.9.pth \
  "class0 class1 class2 ..." img0.jpg output_dir/img0_vis.jpg

# multi images inference
bash scripts/demo.sh \
  projects/ovdino/configs/ovdino_swin_tiny224_bert_base_infer_demo.py \
  ../inits/ovdino/ovdino_swint_ogc-coco50.2_lvismv40.1_lvis32.9.pth \
  "class0 long_class1 long_class2 ..." "img0.jpg img1.jpg" output_dir

# image folder inference
bash scripts/demo.sh \
  projects/ovdino/configs/ovdino_swin_tiny224_bert_base_infer_demo.py \
  ../inits/ovdino/ovdino_swint_ogc-coco50.2_lvismv40.1_lvis32.9.pth \
  "class0 long_class1 long_class2 ..." image_dir output_dir

NOTE: The input category_names are separated by spaces, and the words of single class are connected by underline (_).

Web inference demo.

cd $root_dir/ovdino
bash scripts/app.sh \
  projects/ovdino/configs/ovdino_swin_tiny224_bert_base_infer_demo.py \
  ../inits/ovdino/ovdino_swint_ogc-coco50.2_lvismv40.1_lvis32.9.pth

After the web demo deployment, you can open the demo on your browser.

We also provide the online demo, click and enjoy.

✅ TODO

Release the pre-trained model.
Release the fine-tuning and evaluation code.
Support the local inference demo.
Support the web inference demo.
Release the pre-training code on O365 dataset.
Support ONNX exporting OV-DINO model.
Support OV-DINO in 🤗 transformers.
Release the all pre-training code.

😊 Acknowledge

This project has referenced some excellent open-sourced repos (Detectron2, detrex, GLIP, G-DINO, YOLO-World). Thanks for their wonderful works and contributions to the community.

📌 Citation

If you find OV-DINO is helpful for your research or applications, please consider giving us a star 🌟 and citing it by the following BibTex entry.

@article{wang2024ovdino,
  title={OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion},
  author={Wang, Hao and Ren, Pengzhen and Jie, Zequn and Dong, Xiao and Feng, Chengjian and Qian, Yinlong and Ma, Lin and Jiang, Dongmei and Wang, Yaowei and Lan, Xiangyuan and others},
  journal={arXiv preprint arXiv:2407.07844},
  year={2024}
}

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🦖 OV-DINO

Unified Open-Vocabulary Detection with Language-Aware Selective Fusion

🔥 Updates

🚀 Introduction

📄 Overview

✨ Model Zoo

🏁 Getting Started

1. Project Structure

2. Installation

2. Data Preparing

COCO

LVIS

Objects365

3. Evaluation

Zero-Shot Evaluation on COCO Benchmark

Zero-Shot Evaluation on LVIS Benchmark

4. Fine-Tuning

Fine-Tuning on COCO Dataset

Fine-Tuning on Custom Dataset

5. Pre-Training

Pre-Training on Objects365 dataset

Pre-Training on [Objects365, GoldG] datasets

Pre-Training on [Objects365, GoldG, CC1M‡] datasets

💻 Demo

✅ TODO

😊 Acknowledge

📌 Citation

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datas		datas
docs		docs
inits		inits
ovdino		ovdino
wkdrs		wkdrs
LICENSE		LICENSE
README.md		README.md

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🦖 OV-DINO

Unified Open-Vocabulary Detection with Language-Aware Selective Fusion

🔥 Updates

🚀 Introduction

📄 Overview

✨ Model Zoo

🏁 Getting Started

1. Project Structure

2. Installation

2. Data Preparing

COCO

LVIS

Objects365

3. Evaluation

Zero-Shot Evaluation on COCO Benchmark

Zero-Shot Evaluation on LVIS Benchmark

4. Fine-Tuning

Fine-Tuning on COCO Dataset

Fine-Tuning on Custom Dataset

5. Pre-Training

Pre-Training on Objects365 dataset

Pre-Training on [Objects365, GoldG] datasets

Pre-Training on [Objects365, GoldG, CC1M‡] datasets

💻 Demo

✅ TODO

😊 Acknowledge

📌 Citation

About

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