基于CenterNet的旋转目标检测
本工作初衷是提供一个极其精简的CenterNet代码，并对旋转目标进行检测，1.0为：
${R-CenterNet_ROOT}
|-- backbone
`-- |-- dlanet.py
|-- dlanet_dcn.py
|-- Loss.py
|-- dataset.py
|-- train.py
|-- predict.py
应读者需求，随后更新了2.0
${R-CenterNet_ROOT}
|-- labelGenerator
`-- |-- Annotations
|-- voc2coco.py
|-- evaluation.py
2.0以及data/airplane、imgs、ret文件夹都不是必须的，如果您只是想快速上手，1.0足够了。
- R-DLADCN(推荐)(DCN编译与原版CenterNet保持一致)
- R-ResDCN(主干网用的ResNet而不是DLA)
- R-DLANet(如果你不会编译DCN，就使用这个没有编译DCN的主干网)
- DLADCN.jpg
- 我对CenterNet原版代码 进行了重构，使代码看起来更加简洁。
- 如何编译DCN以及环境需求, 与CenterNet 原版保持一致。
- 关于数据处理与更多细节, 可以参考 here
- torch版本1.2，如果你用的0.4会发生报错。
- 打标签用labelGenerator文件夹里面的代码。
- 修改代码中所有num_classes为你的类别数目，并且修改back_bone中hm的数目为你的类别数，如： def DlaNet(num_layers=34, heads = {'hm': your classes num, 'wh': 2, 'ang':1, 'reg': 2}, head_conv=256, plot=False):
detector for rotated-object based on CenterNet
The original intention of this work is to provide a extremely compact code of CenterNet and detect rotating targets: 1.0
${R-CenterNet_ROOT}
|-- backbone
`-- |-- dlanet.py
|-- dlanet_dcn.py
|-- Loss.py
|-- dataset.py
|-- train.py
|-- predict.py
At the request of readers, 2.0 was subsequently updated：2.0
${R-CenterNet_ROOT}
|-- labelGenerator
`-- |-- Annotations
|-- voc2coco.py
|-- evaluation.py
2.0 and the data/airplane, imgs, ret folders are not required. If you just want to get started quickly, 1.0 is enough。
- R-DLADCN(this code)(How to complie dcn refer to the original code of CenterNet)
- R-ResDCN(just replace cnn in resnet with dcn)
- R-DLANet(not use dcn if you don't know how to complie dcn)
- DLADCN.jpg
- I refactored the original code to make codes more concise.
- How to complie dcn and configure the environment, refer to the original code of CenterNet.
- For data processing and more details, refer to here
- torch version==1.2，don't use version==0.4!
- label your data use labelGenerator;
- modify all num_classes to your classes num, and modify the num of hm in your back_bone, such as: def DlaNet(num_layers=34, heads = {'hm': your classes num, 'wh': 2, 'ang':1, 'reg': 2}, head_conv=256, plot=False):
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