CyDAS Detection Code Base

Environments

• Python 3.7
• Pytorch>=1.8.2
• Torchvision == 0.9.2

You can directly run the code sh env.sh and sh compile.sh to setup the running environment. We use 8 GPUs (24GB RTX 3090) to train our detector, you can adjust the batch size in configs by yourselves.

Data Preparatoin

Your directory tree should be look like this:

$HitDet.pytorch/data
├── coco
│   ├── annotations
│   ├── train2017
│   └── val2017
│
├── VOCdevkit
│   ├── VOC2007
│   │   ├── Annotations
│   │   ├── ImageSets
│   │   ├── JPEGImages
│   │   ├── SegmentationClass
│   │   └── SegmentationObject
│   └── VOC2012
│       ├── Annotations
│       ├── ImageSets
│       ├── JPEGImages
│       ├── SegmentationClass
│       └── SegmentationObject

Getting Start

Our pretrained backbone params can be found in GoogleDrive

Installation

• Clone this repo:

cd CDARTS_detection

• Install dependencies:

bash env.sh
bash compile.sh

Train:

sh train.sh

Acknowledgement

Our code is based on the open source project MMDetection.