official implementation for "ProxyDet: Synthesizing Proxy Novel Classes via Classwise Mixup for Open-Vocabulary Object Detection"
Joonhyun Jeong1,2, Geondo Park2, Jayeon Yoo3, Hyungsik Jung1, Heesu Kim1
1 NAVER Cloud, ImageVision
2 KAIST
3 Seoul National University
- download LVIS + ImageNet-LVIS pretrained weight
- make
models
directory and move weight to themodels
directory - run inference demo as below:
python3 demo.py \
--config-file configs/ProxyDet_R50_Lbase_INL.yaml \
--input .assets/desk.jpg \
--output out.jpg \
--vocabulary custom \
--custom_vocabulary headphone,webcam,paper,coffe \
--confidence-threshold 0.3 \
--zeroshot_weight_path datasets/metadata/lvis_v1_clip_a+cname.npy \
--opts MODEL.WEIGHTS models/proxydet_r50_w_inl.pth
2023/12/09
: got accepted in AAAI-24 🥳2024/01/25
: open ProxyDet code
- Inference and Demo
- Training codes
- Gradio web demo
If you find that this project helps your research, please consider citing as below:
@article{jeong2023proxydet,
title={ProxyDet: Synthesizing Proxy Novel Classes via Classwise Mixup for Open Vocabulary Object Detection},
author={Jeong, Joonhyun and Park, Geondo and Yoo, Jayeon and Jung, Hyungsik and Kim, Heesu},
journal={arXiv preprint arXiv:2312.07266},
year={2023}
}
ProxyDet
Copyright (c) 2024-present NAVER Cloud Corp.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.