https://github.com/visionxlab/whollywood-jittor
[TPAMI] Wholly Leveraging Diversified-quality Labels for Weakly-supervised Oriented Object Detection
https://github.com/visionxlab/whollywood-jittor
Last synced: over 1 year ago
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[TPAMI] Wholly Leveraging Diversified-quality Labels for Weakly-supervised Oriented Object Detection
- Host: GitHub
- URL: https://github.com/visionxlab/whollywood-jittor
- Owner: VisionXLab
- License: apache-2.0
- Created: 2024-08-23T03:38:25.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-02-14T03:54:20.000Z (over 1 year ago)
- Last Synced: 2025-03-27T01:13:49.921Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 3.83 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Wholly-WOOD
## Introduction
We develop **Wholly-WOOD** (**Wholly** Leveraging Diversified-quality Labels for **W**eakly-supervised **O**riented **O**bject **D**etection), a weakly-supervised OOD framework, capable of wholly leveraging various labeling forms (Points, HBoxes, RBoxes, and their combination) in a unified fashion. By only using HBox for training, our Wholly-WOOD achieves performance very close to that of the RBox-trained counterpart on remote sensing and other areas, which significantly reduces the tedious efforts on labor-intensive annotation for oriented objects.
This project is the [Jittor](https://github.com/Jittor/jittor) implementation of Wholly-WOOD. The code works with **Jittor 1.3.8.5**. It is modified from [JDet](https://github.com/Jittor/JDet), which is an object detection benchmark mainly focus on oriented object detection. PyTorch version: [Wholly-WOOD (PyTorch)](https://github.com/yuyi1005/whollywood).
## Models
This repository contains the Wholly-WOOD model and our series of work on weakly-supervised OOD (i.e. H2RBox, H2RBox-v2, and Point2RBox).
### 1. Wholly-WOOD
We can train/test Wholly-WOOD model by:
```shell
python tools/run_net.py --config-file=configs/whollywood/whollywood_obb_r50_adamw_fpn_1x_dota.py --task=train
python tools/run_net.py --config-file=configs/whollywood/whollywood_obb_r50_adamw_fpn_1x_dota.py --task=test
```
### 2. H2RBox
We can train/test H2RBox model by:
```shell
python tools/run_net.py --config-file=configs/whollywood/h2rbox_obb_r50_adamw_fpn_1x_dota.py --task=train
python tools/run_net.py --config-file=configs/whollywood/h2rbox_obb_r50_adamw_fpn_1x_dota.py --task=test
```
### 3. H2RBox-v2
We can train/test H2RBox-v2 model by:
```shell
python tools/run_net.py --config-file=configs/whollywood/h2rbox_v2p_obb_r50_adamw_fpn_1x_dota.py --task=train
python tools/run_net.py --config-file=configs/whollywood/h2rbox_v2p_obb_r50_adamw_fpn_1x_dota.py --task=test
```
### 4. Point2RBox
We can train/test Point2RBox model by:
```shell
python tools/run_net.py --config-file=configs/whollywood/point2rbox_obb_r50_adamw_fpn_1x_dota.py --task=train
python tools/run_net.py --config-file=configs/whollywood/point2rbox_obb_r50_adamw_fpn_1x_dota.py --task=test
```
## Installation
Recommended environments:
* System: **Linux** (e.g. Ubuntu/CentOS/Arch), **macOS**, or **Windows Subsystem of Linux (WSL)**
* Python == 3.10
* Jittor == 1.3.8.5
* CPU Compiler: g++ == 11.4.0
* GPU Library: cuda == 12.3 & cudnn == 8.9.7.29
**Step 1: Install requirements**
```shell
git clone https://github.com/yuyi1005/whollywood-jittor
cd whollywood-jittor
python -m pip install -r requirements.txt
```
If you have any installation problems for Jittor, please refer to [Jittor](https://github.com/Jittor/jittor)
**Step 2: Install Wholly-WOOD**
```shell
cd whollywood-jittor
# suggest this
python setup.py develop
# or
python setup.py install
```
If you don't have permission for install, please add ```--user```.
## Datasets
The following datasets are supported in JDet, please check the corresponding document before use.
DOTA1.0/DOTA1.5/DOTA2.0 Dataset: [dota.md](docs/dota.md).
FAIR Dataset: [fair.md](docs/fair.md)
SSDD/SSDD+: [ssdd.md](docs/ssdd.md)
You can also build your own dataset by convert your datas to DOTA format.
## Visualization
You can test and visualize results on your own image sets by:
```shell
python tools/run_net.py --config-file=configs/whollywood/whollywood_obb_r50_adamw_fpn_1x_dota.py --task=vis_test
```
You can choose the visualization style you prefer, for more details about visualization, please refer to [visualization.md](docs/visualization.md).
## Citation
```
@article{yu2025whollywood,
title={Wholly-WOOD: Wholly Leveraging Diversified-quality Labels for Weakly-supervised Oriented Object Detection},
author={Yi Yu and Xue Yang and Yansheng Li and Zhenjun Han and Feipeng Da and Junchi Yan},
year={2025},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
}
```