### Create and Activate Conda Environment

Source: https://github.com/taowangzj/llformerv2/blob/main/README.md

Create a new conda environment named 'LLFormerv2' with Python 3.8 and activate it. Then, install the project dependencies from 'requirements.txt'.

```bash
conda create -n LLFormerv2 python=3.8
conda activate LLFormerv2
pip install -r requirements.txt
```

--------------------------------

### Train LLFormerV2 Model for Non-UHD Datasets

Source: https://github.com/taowangzj/llformerv2/blob/main/README.md

Train the LLFormerV2 model for non-UHD datasets like LOL and MIT-Adobe FiveK. Adjust the configuration file and the number of GPUs as needed.

```bash
sh train.sh
```

--------------------------------

### Calculate Metrics for Test Results (RGB Channel)

Source: https://github.com/taowangzj/llformerv2/blob/main/README.md

Run the 'measure.py' script to calculate performance metrics for the test results on the RGB channel. Make sure to update the input and ground truth data paths in the script.

```bash
python measure.py
```

--------------------------------

### Test LLFormerV2 Model

Source: https://github.com/taowangzj/llformerv2/blob/main/README.md

Execute the test script to evaluate the pre-trained LLFormerV2 model. Ensure to specify the correct configuration file and data paths.

```bash
sh test.sh
```

--------------------------------

### Clone LLFormerV2 Repository

Source: https://github.com/taowangzj/llformerv2/blob/main/README.md

Clone the LLFormerV2 repository from GitHub to your local machine.

```bash
git clone https://github.com/TaoWangzj/LLFormerV2.git
cd LLFormerv2
```

--------------------------------

### Calculate Metrics for Test Results (Y Channel)

Source: https://github.com/taowangzj/llformerv2/blob/main/README.md

Run the 'measure.py' script with the '--y' flag to calculate performance metrics for the test results on the Y channel. Ensure the input and ground truth data paths are correctly set.

```bash
python measure.py --y
```

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