Update README.md

README.md

@@ -65,8 +65,9 @@ Here is an example if you want to make own dataset.
 
 ```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
+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
 ```
@@ -109,14 +110,15 @@ If you want to register your own dataset,
 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 mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py -d mmsegmentation/configs/_base_/datasets/CVRP_pipeline.py -c 2 -w mmsegmentation/work_dirs -s mmsegmentation/CVRP_configs
+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 mmsegmentation/configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512.py -d mmsegmentation/configs/_base_/datasets/CVRP_pipeline.py -c 2 -w mmsegmentation/work_dirs -s mmsegmentation/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 mmsegmentation/configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py -d mmsegmentation/configs/_base_/datasets/CVRP_pipeline.py -c 2 -w mmsegmentation/work_dirs -s mmsegmentation/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 mmsegmentation/configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py -d mmsegmentation/configs/_base_/datasets/CVRP_pipeline.py -c 2 -w mmsegmentation/work_dirs -s mmsegmentation/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
@@ -133,8 +135,7 @@ Also, you can download checkpoint [here](https://huggingface.co/CVRPDataset/Mode
 
 ***Test***
 ```bash
-cd ..
-python run/test.py -d mmsegmentation/CVRPDataset/val -m mmsegmentation/CVRP_configs/CVRP_mask2former.py -pth mmsegmentation/work_dirs/CVRP_mask2former/Mask2Former.pth -o mmsegmentation/outputs/CVRP_mask2former
+python ../run/test.py -d mmsegmentation/CVRPDataset/val -m mmsegmentation/CVRP_configs/CVRP_mask2former.py -pth mmsegmentation/work_dirs/CVRP_mask2former/Mask2Former.pth -o mmsegmentation/outputs/CVRP_mask2former
 ```
 ### UI
 ##### We create a web user interface for annotation based on gradio: