EasyDAM_dataset

The dataset is used in the paper "EasyDAM_V4: Guided-GAN based cross-species data labeling for fruit detection with significant shape difference". (in Press)

Dataset and Pretrained models available here:

Please fill out this form to get the download link.

We also provide examples of automatic labeling images as follows.

Install

Clone this repo.

git clone https://github.com/I3-Laboratory/EasyDAM_dataset

Create a new conda environment and activate the environment.

conda create --name EasyDAM_V4 python=3.6
conda activate EasyDAM_V4

Install pytorch 0.4.1 and COCOAPI.

conda install pytorch=0.4.1 torchvision -c pytorch
git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
cd $COCOAPI/PythonAPI  
make  
python setup.py install --user   

Install the requirements.

pip install -r requirements.txt

Compile deformable convolutional (from DCNv2).

cd $EasyDAM_dataset/CenterNet/src/lib/models/networks/DCNv2  
./make.sh

Usage

We support demo for automatic labeling.

First, download the pretrained models and put them in EasyDAM_dataset/CenterNet/exp/.

Then, run:

python demo.py ctdet --demo /path/to/image --load_model ../models/ctdet_coco_dla_2x.pth