Update README.md

README.md

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-## CVRP: A Rice Image Dataset with High-Quality Annotations for Image Segmentation and Plant Phenomics Research
-
-##### Multi-cultivar and multi-view rice plant image dataset (CVRP) consists of 2,303 field images with their annotated masks and 123 indoor images of individual panicles.
-
-
-
-### Annotation Workflow
-##### To optimize the process of annotation, we combine deep learning methods with manual curation. The workflow comprises two stages: manual annotation and model-based prediction followed by manual curation. 
-![alt text](img/workflow.png)
-### Getting Started
-##### Recommend python 3.7, CUDA v11.3 and Pytorch 1.10.0.
-***Clone***
-
-```bash
-git clone https://huggingface.co/CVRPDataset/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
-
-pip install -r run/requirements.txt
-cd mmsegmentation
-pip install -v -e .
-```
-***Creating a Dataset***
-
-1. Directory structure of the dataset:
-<pre>
-   📁 CVRPDataset/
-   ├─📁 images/
-   └─📁 labelme_jsons/
-</pre>
-1. Convert labelme files to mask:
-   ```bash
-   python run/labelme2mask.py
-   ```
-   now, the structure looks like:
-<pre>
-   📁 CVRPDataset/
-   ├─📁 img_dir/
-   └─📁 ann_dir/
-</pre>
-1. Split the training set and test set.
-   ```bash
-   python run/split_dataset.py
-   ```
-     now, the structure looks like:
-<pre>
-   📁 CVRPDataset/
-   ├─📁 img_dir/
-   │  ├─📁 train/
-   │  └─📁 val/
-   └─📁 ann_dir/
-      ├─📁 train/
-      └─📁 val/
-</pre>
-
-
-   You can download our training set and test set  [here](http://61.155.111.202:18080/cvrp/dataset).
-
-***Dataset Configs***
-
-```bash
-cd mmseg/datasets
-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
-```
-If you want to register your own dataset, 
-1. Import and register datasets in `mmseg/datasets/__init__.py`
-   ```python
-   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_/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
-mkdir 'work_dirs' 'CVRP_configs' 'outputs'
-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](https://huggingface.co/CVRPDataset/Model/tree/main/model_configs). 
- ```bash 
-cd CVRP_configs
-wget https://huggingface.co/CVRPDataset/Model/resolve/main/model_configs/CVRP_mask2former.py
- ```
-
-***Train***
-```bash 
-python tools/train.py CVRP_configs/CVRP_mask2former.py
-```
-Also, you can download checkpoint [here](https://huggingface.co/CVRPDataset/Model/tree/main/checkpoint). 
-
-***Test***
-```bash
-python ../run/test.py -d CVRPDataset/val -m CVRP_configs/CVRP_mask2former.py -pth work_dirs/CVRP_mask2former/Mask2Former.pth -o outputs/CVRP_mask2former
-```
-### UI
-##### We create a web user interface for annotation based on gradio:
-```bash
-python app.py
-```
-##### The UI :
-1.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*.
-2.We provide two forms of segmentation results for download at ③.
-![alt text](img/app.png)
-
-### 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
-```
-
-```bash
-python run/json2png.py
-```
-### Citation
-Please considering cite our paper if you find this work useful!
-
-### Acknowledgements
-We thank Mr.Zhitao Zhu, Dr. Weijie Tang, and Dr. Yunhui Zhang for their technical support.
+## CVRP: A Rice Image Dataset with High-Quality Annotations for Image Segmentation and Plant Phenomics Research
+
+##### Multi-cultivar and multi-view rice plant image dataset (CVRP) consists of 2,303 field images with their annotated masks and 123 indoor images of individual panicles.
+
+
+
+### Annotation Workflow
+##### To optimize the process of annotation, we combine deep learning methods with manual curation. The workflow comprises two stages: manual annotation and model-based prediction followed by manual curation. 
+![alt text](img/workflow.png)
+### Getting Started
+##### Recommend python 3.7, CUDA v11.3 and Pytorch 1.10.0.
+***Clone***
+
+```bash
+git clone https://huggingface.co/CVRPDataset/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
+
+pip install -r run/requirements.txt
+cd mmsegmentation
+pip install -v -e .
+```
+***Creating a Dataset***
+
+1. Directory structure of the dataset:
+<pre>
+   📁 CVRPDataset/
+   ├─📁 images/
+   └─📁 labelme_jsons/
+</pre>
+1. Convert labelme files to mask:
+   ```bash
+   python run/labelme2mask.py
+   ```
+   now, the structure looks like:
+<pre>
+   📁 CVRPDataset/
+   ├─📁 img_dir/
+   └─📁 ann_dir/
+</pre>
+1. Split the training set and test set.
+   ```bash
+   python run/split_dataset.py
+   ```
+     now, the structure looks like:
+<pre>
+   📁 CVRPDataset/
+   ├─📁 img_dir/
+   │  ├─📁 train/
+   │  └─📁 val/
+   └─📁 ann_dir/
+      ├─📁 train/
+      └─📁 val/
+</pre>
+
+
+   You can download our training set and test set  [here](http://61.155.111.202:18080/cvrp/dataset).
+
+***Dataset Configs***
+
+```bash
+cd mmseg/datasets
+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
+```
+If you want to register your own dataset, 
+1. Import and register datasets in `mmseg/datasets/__init__.py`
+   ```python
+   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_/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
+mkdir 'work_dirs' 'CVRP_configs' 'outputs'
+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](https://huggingface.co/CVRPDataset/Model/tree/main/model_configs). 
+ ```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](https://huggingface.co/CVRPDataset/Model/tree/main/checkpoint). 
+
+***Test***
+```bash
+cd ..
+python run/test.py -d CVRPDataset/val -m CVRP_configs/CVRP_mask2former.py -pth work_dirs/CVRP_mask2former/Mask2Former.pth -o outputs/CVRP_mask2former
+```
+### UI
+##### We create a web user interface for annotation based on gradio:
+```bash
+python app.py
+```
+##### The UI :
+1.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*.
+2.We provide two forms of segmentation results for download at ③.
+![alt text](img/app.png)
+
+### 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
+```
+
+```bash
+python run/json2png.py
+```
+### Citation
+Please considering cite our paper if you find this work useful!
+
+### Acknowledgements
+We thank Mr.Zhitao Zhu, Dr. Weijie Tang, and Dr. Yunhui Zhang for their technical support.