| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| pretty_name: BOCCHI Motion-Blur Detection |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - image-segmentation |
| tags: |
| - motion-blur |
| - local-blur-detection |
| - DCT |
| - HiFST |
| - MSDCT-UNet |
| - accv2026 |
| --- |
| |
| # BOCCHI Motion-Blur Detection |
|
|
| Companion dataset for the ACCV 2026 submission |
| *"MSDCT-UNet: Multi-Scale DCT U-Net for Local Motion Blur Detection"*. |
|
|
| This repo ships **both raw image+mask pairs and pre-computed PKL feature |
| caches**, so you can either train from scratch or skip preprocessing |
| entirely. |
|
|
| **Code release:** <https://huggingface.co/aianonymous12/msdct-unet> |
| (pretrained weights for the same architecture). |
|
|
| --- |
|
|
| ## Repository layout |
|
|
| ``` |
| aianonymous12/BOCCHI/ |
| ├── README.md |
| ├── BOCCHI_dataset/ ← OUR dataset (BOCCHI, CC BY-NC 4.0) |
| │ ├── img/ 0001.jpg ... 0633.jpg # 633 RGB images |
| │ ├── mask/ 0001.png ... 0633.png # 633 binary masks (0 = sharp, 255 = blur) |
| │ └── all_data.pkl # pre-built feature cache (8.4 GB) |
| ├── Inference_dataset/merged/ ← Cross-eval set (mixed sources) |
| │ ├── img_all/ 422 jpg # 164 BOCCHI-protocol + 162 ReLoBlur test |
| │ ├── mask_all/ 422 png # + 96 BlurDataset held-out |
| │ └── all_data.pkl # pre-built (5.6 GB) |
| ├── ReLoBlur_dataset/train/ |
| │ └── all_data.pkl # pre-built only (16 GB) — third-party |
| └── BlurDataset/ |
| └── all_data.pkl # pre-built only (2.7 GB) — third-party |
| ``` |
|
|
| | Subset | Samples | Raw size | PKL size | |
| |-----------------------|--------:|---------:|---------:| |
| | BOCCHI (BOCCHI_dataset) | 633 | 109 MB | 8.4 GB | |
| | Inference (merged) | 422 | 97 MB | 5.6 GB | |
| | ReLoBlur (train) | 1200 | — | 16 GB | |
| | BlurDataset | 200 | — | 2.7 GB | |
| |
| > Raw img+mask for BOCCHI and Inference are bundled (CC BY-NC 4.0). |
| > ReLoBlur and BlurDataset raw data are **not** included (third-party |
| > licenses); only the derived PKL features are redistributed. |
| |
| --- |
| |
| ## Quick start |
| |
| ```bash |
| pip install huggingface_hub |
|
|
| # Minimum: BOCCHI + Inference PKLs (14 GB) — reproduces Table 2 BOCCHI + Table 3 |
| huggingface-cli download aianonymous12/BOCCHI --repo-type dataset \ |
| BOCCHI_dataset/all_data.pkl \ |
| Inference_dataset/merged/all_data.pkl \ |
| --local-dir data |
| |
| # Raw img+mask only (205 MB, no PKLs) — for building your own features |
| huggingface-cli download aianonymous12/BOCCHI --repo-type dataset \ |
| --include "BOCCHI_dataset/img/*" "BOCCHI_dataset/mask/*" \ |
| "Inference_dataset/merged/img_all/*" "Inference_dataset/merged/mask_all/*" \ |
| --local-dir data |
| |
| # Everything (~33 GB) |
| huggingface-cli download aianonymous12/BOCCHI --repo-type dataset --local-dir data |
| ``` |
| |
| Then follow §4 of the code release README to reproduce paper Tables 2 / 3. |
| |
| --- |
| |
| ## PKL internal schema |
| |
| ```python |
| { |
| "<stem>": { |
| "img": np.ndarray (H, W, 3) uint8, # RGB |
| "msk": np.ndarray (H, W) uint8 {0,255}, # 0 = sharp, 255 = blur |
| "DCT_coef": np.ndarray (Wgrid, Hgrid, 57) float32, # HiFST DCT features |
| }, |
| "settings": {"mode": "rotate"/"pad", "size": (720, 1080), |
| "num_scales": 4, "scale_start": 2, ...} |
| } |
| ``` |
| |
| The 57-channel layout is the legacy `(Wgrid, Hgrid, 57)` format; the loader |
| in the code release (`utils/dataset.py`) auto-detects this and permutes to |
| `(57, Hgrid, Wgrid)` PyTorch convention at `__getitem__` time. |
|
|
| --- |
|
|
| ## License |
|
|
| - **BOCCHI** (`BOCCHI_dataset/img/`, `BOCCHI_dataset/mask/`, `BOCCHI_dataset/all_data.pkl`) |
| and the **BOCCHI-protocol portion of the Inference set**: CC BY-NC 4.0 |
| (our own data, free for non-commercial research use with attribution). |
| - **ReLoBlur** PKL: derivative of Li et al., AAAI 2023. Redistributed |
| here under the original authors' terms for review purposes. Please cite |
| the original ReLoBlur paper if you use it. |
| - **BlurDataset** PKL: derivative of the CUHK blur-detection benchmark. |
| Same conditions as above. |
|
|
| The reviewer / reproducer must comply with each source dataset's license |
| when using the corresponding subset. |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{anon2026msdctunet, |
| title = {MSDCT-UNet: Multi-Scale DCT U-Net for Local Motion Blur Detection}, |
| author = {Anonymous}, |
| booktitle = {ACCV}, |
| year = {2026} |
| } |
| ``` |
|
|
| ## Note |
|
|
| Hosted on an anonymous reviewer account for the ACCV 2026 double-blind |
| review. Author identities will be revealed at camera-ready. |
|
|
