pip install huggingface_hub
huggingface-cli download CVRPDataset/Model --local-dir /your/path/to/save/Model
cd Model
git clone https://github.com/open-mmlab/mmsegmentation.git -b v1.1.2 mmsegmentation

pip install -U openmim
mim install mmengine
mim install mmcv==2.0.0

cd mmsegmentation
pip install -v -e .
pip install "mmdet>=3.0.0rc4"

We create a web user interface for annotation based on gradio.

bash

pip install gradio
python app.py

The UI :

Users can upload an image or use a sample image at ①.Then, they can select one of four models at ②. We recommend Mask2Former. After that, click Run.
We provide two forms of segmentation results for download at ③.
alt text

Train and Test

Creating a Dataset

Here is an example if you want to make own dataset.

① Directory structure of the dataset:

📁 CVRPDataset/ ├─📁 images/ └─📁 labelme_jsons/

② Convert labelme files to mask:

python run/labelme2mask.py

now, the structure looks like:

📁 CVRPDataset/ ├─📁 img_dir/ └─📁 ann_dir/

③ Split the training set and test set.

python run/split_dataset.py

now, the structure looks like:

📁 CVRPDataset/ ├─📁 img_dir/ │ ├─📁 train/ │ └─📁 val/ └─📁 ann_dir/ ├─📁 train/ └─📁 val/

If you have annotations in RGB format, you need

python run/transform.py

and then follow split the train set and evaluation set according to the format in ③. You can also download our training set and validation set here.

Dataset Configs

bash

cd mmsegmentation/mmseg/datasets
rm -rf __init__.py                  # delete original file
wget https://huggingface.co/CVRPDataset/Model/resolve/main/dataset_configs/CVRP.py
wget https://huggingface.co/CVRPDataset/Model/resolve/main/dataset_configs/__init__.py  
cd ../../configs/_base_/datasets
wget https://huggingface.co/CVRPDataset/Model/resolve/main/dataset_configs/CVRP_pipeline.py

① If you want to register your own dataset, import and register datasets in mmseg/datasets/__init__.py

from .CVRP import CVRPDataset

Add,

python

# other datasets
__all__ = ['CVRPDataset']

② Register dataset class in `mmseg/datasets/CVRP.py'

python

class CVRPDataset(BaseSegDataset):
METAINFO = {
     'classes':['background','panicle'],
     'palette':[[127,127,127],[200,0,0]]
 }

③ Modify pipeline of data process in config/_base_/datasets/CVRP_pipeline.py

python

 dataset_type = 'CVRPDataset' 
 data_root = 'CVRPDataset/'

you'll need to specify the paths for the train and evalution data directories.

python

  # train_dataloader: 
  data_prefix=dict(img_path='img_dir/train', seg_map_path='ann_dir/train')
  # val_dataloader:
  data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val')

Model Configs

You can generate model config files using run_configs.py

bash

cd mmsegmentation
mkdir 'work_dirs' 'CVRP_configs' 'outputs' 'CVRPDataset'
python ../run/run_configs.py --model_name deeplabv3plus -m configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py -d configs/_base_/datasets/CVRP_pipeline.py -c 2 -w work_dirs -s CVRP_configs

python ../run/run_configs.py --model_name knet -m configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512.py -d configs/_base_/datasets/CVRP_pipeline.py -c 2 -w work_dirs -s CVRP_configs

python ../run/run_configs.py --model_name mask2former -m configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py -d configs/_base_/datasets/CVRP_pipeline.py -c 2 -w work_dirs -s CVRP_configs

python ../run/run_configs.py --model_name segformer -m configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py -d configs/_base_/datasets/CVRP_pipeline.py -c 2 -w work_dirs -s CVRP_configs

Also, you can download model config files here.

bash

cd CVRP_configs
wget https://huggingface.co/CVRPDataset/Model/resolve/main/model_configs/CVRP_mask2former.py

Train

bash

cd mmsegmentation 
python tools/train.py CVRP_configs/CVRP_mask2former.py

Also, you can download checkpoint here.

bash

cd work_dirs
mkdir CVRP_mask2former
cd CVRP_mask2former
wget https://huggingface.co/CVRPDataset/Model/resolve/main/checkpoint/Mask2Former.pth

Test

python ../run/test.py -d CVRPDataset/val -m CVRP_configs/CVRP_mask2former.py -pth work_dirs/CVRP_mask2former/Mask2Former.pth -o outputs/CVRP_mask2former

LabelMe

If you need to manually adjust the annotation, you can use LabelMe.

bash

python run/mask2json.py
pip install labelme==3.16.7
labelme

python run/json2png.py

Data and Code Availability

The CVRP dataset and accompanying codes are publicly available from Hugging Face at https://huggingface.co/datasets/CVRPDataset/CVRP & https://huggingface.co/CVRPDataset/Model, and Bioinformatics service in Nanjing Agricultural University at http://bic.njau.edu.cn/CVRP.html.

Acknowledgements

We thank Mr.Zhitao Zhu, Dr. Weijie Tang, and Dr. Yunhui Zhang for their technical support.