Learning Object-Language Alignments for Open-Vocabulary Object Detection,
Chuang Lin, Peize Sun, Yi Jiang, Ping Luo, Lizhen Qu, Gholamreza Haffari, Zehuan Yuan, Jianfei Cai,
ICLR 2023 (https://arxiv.org/abs/2211.14843)
We are excited to announce that our paper was accepted to ICLR 2023! 🥳🥳🥳
vldet_demo.mp4
- Linux or macOS with Python ≥ 3.7
- PyTorch ≥ 1.9. Install them together at pytorch.org to make sure of this. Note, please check PyTorch version matches that is required by Detectron2.
- Detectron2: follow Detectron2 installation instructions.
conda create --name VLDet python=3.7 -y
conda activate VLDet
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c nvidia
# under your working directory
git clone https://github.com/clin1223/VLDet.git
cd VLDet
cd detectron2
pip install -e .
cd ..
pip install -r requirements.txt-
Directly learn an open-vocabulary object detector from image-text pairs by formulating the task as a bipartite matching problem.
-
State-of-the-art results on Open-vocabulary LVIS and Open-vocabulary COCO.
-
Scaling and extending novel object vocabulary easily.
Please first prepare datasets.
The VLDet models are finetuned on the corresponding Box-Supervised models (indicated by MODEL.WEIGHTS in the config files). Please train or download the Box-Supervised model and place them under VLDet_ROOT/models/ before training the VLDet models.
To train a model, run
python train_net.py --num-gpus 8 --config-file /path/to/config/name.yaml
To evaluate a model with a trained/ pretrained model, run
python train_net.py --num-gpus 8 --config-file /path/to/config/name.yaml --eval-only MODEL.WEIGHTS /path/to/weight.pth
Download the trained network weights here.
| OV_COCO | box mAP50 | box mAP50_novel |
|---|---|---|
| config_RN50 | 45.8 | 32.0 |
| OV_LVIS | mask mAP_all | mask mAP_novel |
|---|---|---|
| config_RN50 | 30.1 | 21.7 |
| config_Swin-B | 38.1 | 26.3 |
If you find this project useful for your research, please use the following BibTeX entry.
@article{VLDet,
title={Learning Object-Language Alignments for Open-Vocabulary Object Detection},
author={Lin, Chuang and Sun, Peize and Jiang, Yi and Luo, Ping and Qu, Lizhen and Haffari, Gholamreza and Yuan, Zehuan and Cai, Jianfei},
journal={arXiv preprint arXiv:2211.14843},
year={2022}
}
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This repository was built on top of Detectron2, Detic, RegionCLIP and OVR-CNN. We thank for their hard work.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent