| # [ECCV 2026] CUST : Clustered Unit-level Similarity Transformer for Lightweight Image Super-Resolution |
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| Author : Jeongsoo Kim |
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| Our project has been accepted as a poster presentation at ECCV 2026. |
| You can see our paper at [here](https://huggingface.co/papers/2607.11088)(huggingface) |
| or [here](https://arxiv.org/abs/2607.11088)(arXiv). |
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| ## Requirements |
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| ``` |
| # Install Packages |
| pip install -r requirements.txt |
| pip install matplotlib |
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| # Install BasicSR |
| python3 setup.py develop |
| ``` |
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| ## Dataset |
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| We use DIV2K as Training dataset. |
| You can download the dataset at https://github.com/dslisleedh/Download_df2k/blob/main/download_df2k.sh |
| and prepare other test datasets at https://github.com/XPixelGroup/BasicSR/blob/master/docs/DatasetPreparation.md#Common-Image-SR-Datasets |
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| And also, you'd better extract subimages using |
| ``` |
| python3 scripts/data_preparation/extract_subimages.py |
| ``` |
| By running the code above, you may get subimages of training datasets. |
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| ## Pretrained Models |
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| Pre-trained models can be downloaded from ```experiments/pretrained_model```. |
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| ## Training and Test |
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| You can train our CUST following commands below |
| ``` |
| python3 basicsr/train.py -opt options/train/CUST/cust_base(plus, small)_x2(3,4).yml |
| ``` |
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| ### Test |
| You can test our CUST following commands below |
| ``` |
| python3 basicsr/test.py -opt options/test/CUST_base(small)/test_base(small)_benchmark_x2(3, 4).yml |
| ``` |
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| ## Results |
| ### Result Table with #Param and #FLOPs |
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| ### Result Table with GPU Consumption and AVG Inference Time |
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| ### Qualtitative Results |
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| ## Inference Results |
| We will provide visual results of CUST_Base soon. |
| If you want to see only architecture, please refer to `CUST_arch.py`. |
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