File size: 1,941 Bytes
5528edf 5c73e4b 5528edf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | # [ECCV 2026] CUST : Clustered Unit-level Similarity Transformer for Lightweight Image Super-Resolution
Author : Jeongsoo Kim
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).
## Requirements
```
# Install Packages
pip install -r requirements.txt
pip install matplotlib
# Install BasicSR
python3 setup.py develop
```
## Dataset
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
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.
## Pretrained Models
Pre-trained models can be downloaded from ```experiments/pretrained_model```.
## Training and Test
You can train our CUST following commands below
```
python3 basicsr/train.py -opt options/train/CUST/cust_base(plus, small)_x2(3,4).yml
```
### 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
```
## Results
### Result Table with #Param and #FLOPs
### Result Table with GPU Consumption and AVG Inference Time
### Qualtitative Results
## 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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