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https://github.com/DDGRCF/YOLOX_OBB
https://zhuanlan.zhihu.com/p/430850089
https://github.com/DDGRCF/YOLOX_OBB
Last synced: 12 days ago
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https://zhuanlan.zhihu.com/p/430850089
- Host: GitHub
- URL: https://github.com/DDGRCF/YOLOX_OBB
- Owner: DDGRCF
- License: apache-2.0
- Created: 2021-10-07T09:53:12.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2022-11-15T06:55:21.000Z (over 1 year ago)
- Last Synced: 2024-02-29T09:36:00.040Z (4 months ago)
- Language: Python
- Homepage:
- Size: 86.1 MB
- Stars: 140
- Watchers: 3
- Forks: 18
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-yolo-object-detection - DDGRCF/YOLOX_OBB - - YOLOX 旋转框 | 实例分割。 "知乎「刀刀狗」《[YOLOX OBB -- YOLOX 旋转框检测 超详细！！！](https://zhuanlan.zhihu.com/p/430850089)》"。 (Applications)
README
**YOLOX OBB -- YOLOX 旋转框 | 实例分割**
***
![]()
***
## **ForeWord**
More rotated detection methods can reference [OBBDetection](https://github.com/jbwang1997/OBBDetection.git). And you can reference [知乎](https://zhuanlan.zhihu.com/p/430850089?) for more information🔥🔥🔥(知乎更加详细，大家请参考知乎)
## **Introduction**
### Method
* **OBB** OBB -> PolyIoU Loss(OBBDetection) \ KLD Loss(NeurIPS2021) \ GWD Loss(ICML2021)
* **Inst** Inst-> SparseInst(CVPR2022) \ CondInst(ECCV2020) \ BoxInst(CVPR2021)
## **Content**
- [Quick Start](#Quick Start)
- [Instruction](#Instruction)
- [Data](#Data)
- [Demo](#Demo)
- [Train](#Train)
- [Test](#Test)
- [Deploy](#Deploy)
- [Ralated Hub](#Ralated Hub)
## **Quick Start**
Firstly, create python environment
```shell
conda create -n yolox_dect python=3.7 -y
```
then, install pytorch according to your machine, as cuda-10.2 and pytorch-1.7.0, you can install like following
```shell
conda activate yolox_dect
conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.2 -c pytorch -y
```
then, clone the github of the item and install requirements
```shell
git clone --recursive https://github.com/DDGRCF/YOLOX_OBB.git
cd YOLOX_OBB
pip3 install -U pip && pip3 install -r requirements.txt
pip3 install -v -e .
```
install BboxToolkit
```shell
cd BboxToolkit
python setup.py develop
```
## **Instruction**
### **Data**
#### **Convert Other data format into dota style**
If We want to train your datasets, firstly you first convert your data as dota datasets format. If you have a coco annotation-style datasets, you can just convert it annoatations into dota format. We perpare a script for you.
```shell
$ cd my_exps
$ bash coco2dota.sh
# PS: you should change filename、diranme and so on.
```
#### **Convert dota style into BboxToolkit style**
This part please reference [BboxToolkit](./BboxToolkit/USAGE.md)
### **Demo**
I prepare the shell the demo script so that you can quick run obb demo as :
```shell
$ expn=... && exp=... && ckpt=... && cuda=... && img_path=...
$ bash my_exps/demo.sh ${expn} ${exp} ${ckpt} ${cuda} ${img_path} --output_format obb --save_result
```
### **Train**
```shell
$ expn=... && exp=... && cuda=... && num_device=... && batch_size=...
$ bash my_exps/train.sh ${expn} ${exp} ${cuda} ${num_device} ${batch_size} --fp16[optional]
```
### **Test**
#### **OBB**
* eval online
```shell
$ expn=... && exp=... && ckpt=... && cuda=...
$ bash my_exps/eval_obb.sh ${expn} ${exp} ${ckpt} ${cuda} ${num_device} ${batch_size} --fuse[optional] --fp16[optional] --options is_merge=True
```
* generate submission file for *obb*
```shell
$ expn=... && exp=... && ckpt=... && cuda=... && num_device=... && batch_size=...
$ bash my_exps/eval_obb.sh ${expn} ${exp} ${ckpt} ${cuda} ${num_device} ${batch_size} --fuse[optional] --fp16[optional] --options is_merge=True is_submiss=True --test
```
### **Deploy**
* [yolox_s_obb](/exps/example/yolox_obb/yolox_s_dota1_0.py)*:*
- [x] [TensorRT](/demo/OBB/tensorrt)
- [x] [NCNN](/demo/OBB/ncnn)
## **Results**
[MODEL_ZOO](https://pan.baidu.com/s/1k1k1JCq56Z-g9NrRtHNWhQ) | code: `tdm6`
|Model | image size | mAP | epochs |
| ------ |:---: | :---: | :---: |
|[YOLOX_s_dota1_0](./exps/example/yolox_obb/yolox_s_dota1_0.py) |1024 | 70.82(73.17) | 80(137) |
|[YOLOX_s_dota2_0](./exps/example/yolox_obb/yolox_s_dota2_0.py) |1024 | 49.52 | 80 |
|[YOLOX_s_condinst_coco](./exps/example/yolox_obb/yolox_s_dota2_0.py) |1024 | 26.43 | 36 |
|[YOLOX_s_sparseinst_coco](./exps/example/yolox_obb/yolox_s_dota2_0.py) |1024 | 0.05(**error**) | 24 |
more results, wait...
## **Ralated Hub**
- [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX.git)
- [OBBDetection](https://github.com/jbwang1997/OBBDetection.git)
- [BboxToolkit](https://github.com/jbwang1997/BboxToolkit.git)