https://github.com/humansensinglab/agenda
[ICCV 2025] Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision
https://github.com/humansensinglab/agenda
aerial-imagery datasets domain-adaptation-object-detection stable-diffusion
Last synced: about 1 month ago
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[ICCV 2025] Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision
- Host: GitHub
- URL: https://github.com/humansensinglab/agenda
- Owner: humansensinglab
- Created: 2025-05-27T18:30:19.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-09-01T03:10:46.000Z (about 1 month ago)
- Last Synced: 2025-09-01T05:47:47.517Z (about 1 month ago)
- Topics: aerial-imagery, datasets, domain-adaptation-object-detection, stable-diffusion
- Language: Python
- Homepage: https://humansensinglab.github.io/AGenDA/
- Size: 14.7 MB
- Stars: 8
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AGenDA
This is the official code for our ICCV 2025 paper:
> [Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision](https://humansensinglab.github.io/AGenDA/)
> Xiao Fang, Minhyek Jeon, Zheyang Qin, Stanislav Panev, Celso M de Melo, Shuowen Hu, Shayok Chakraborty, Fernando De la Torre
## Requirement
```
# Create virtual environment
conda create -n agenda python=3.9
# Install torch
conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia
# Install dependencies
pip install -r requirements.txt
# Install mmengine and mmcv
mim install mmengine
mim install "mmcv>=2.0.0"
# Install mmdetection
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip install -v -e .
# Install mmyolo
git clone https://github.com/open-mmlab/mmyolo.git
cd mmyolo
pip install -v -e .
```
## Data preparation
Please follow the instruction [here](Data/README.md).
## Usage
### Stage 1: Data generation
Please follow the instruction [here](data_generation/README.md).
### Stage 2: Data annotation
Please follow the instruction [here](data_annotation/README.md).
We upload all checkpoints [here](https://huggingface.co/collections/xiaofanghf/agenda-68a1f2b4f46e657d68ae0875). For more usage details, please go through each stage.
## Citation
Please cite the paper if you use the code and datasets.
```
@misc{fang2025adaptingvehicledetectorsaerial,
title={Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision},
author={Xiao Fang and Minhyek Jeon and Zheyang Qin and Stanislav Panev and Celso de Melo and Shuowen Hu and Shayok Chakraborty and Fernando De la Torre},
year={2025},
eprint={2507.20976},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2507.20976},
}
```
## Acknowledgement
The code is built on [diffusers](https://github.com/huggingface/diffusers/tree/main/examples), [DAAM](https://github.com/castorini/daam), and [AttnDreamBooth](https://github.com/lyuPang/AttnDreamBooth), thanks for their amazing work!