Datasets:
Tasks:
Image-to-Image
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
ArXiv:
License:
Tighten legal language in Disclaimer: avoid unverifiable copyright-ownership claims
cd0b5bb verified | language: | |
| - en | |
| license: mit | |
| pretty_name: RealRain-1k (Unofficial Mirror) | |
| task_categories: | |
| - image-to-image | |
| tags: | |
| - image-restoration | |
| - deraining | |
| - rain-removal | |
| - computer-vision | |
| - real-world | |
| - pytorch | |
| - clearview | |
| size_categories: | |
| - 1K<n<10K | |
| # RealRain-1k: Real-World Single-Image Deraining Dataset (Unofficial Mirror) | |
| <p align="center"> | |
| <img src="banner.jpg" alt="RealRain-1k sample heavy/light rain and clean triples"/> | |
| </p> | |
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| > **Unofficial redistribution of RealRain-1k**, the real-world rain/rain-free image dataset from Li et al. (arXiv 2022), packaged for direct use with [ClearView](https://github.com/dronefreak/clearview)'s dataset pipeline. | |
| ## Disclaimer | |
| This repository is **not** an official release of RealRain-1k. | |
| RealRain-1k was created by Wei Li, Qiming Zhang, Jing Zhang, Zhen Huang, Xinmei Tian, and Dacheng Tao. This repository does **not** claim ownership of any images or metadata, and attributes the dataset to its original creators. (We describe them as "creators" rather than "copyright holders" deliberately — copyright in academic datasets can rest with an author's university, a funding body, or otherwise, under arrangements we have no way to verify from the outside. The official repository's MIT license, discussed under [License](#license) below, is what actually governs redistribution here.) | |
| This repository exists to provide a directly downloadable mirror — the official dataset is distributed via OneDrive/Google Drive folder links rather than a versioned, scriptable source, which is inconvenient for reproducible pipelines and CI. | |
| **Single-hop provenance.** Unlike this collection's SPA-Data mirror, this one does not appear to involve a subsampled or third-party intermediate copy: every split's pair count (784/112/224 per density track) matches the official paper's reported totals exactly, with no evidence of subsampling. We're not able to cryptographically verify it is byte-identical to the official OneDrive/Google Drive release, but the counts line up exactly with what's published. | |
| --- | |
| # Dataset Description | |
| RealRain-1k is a **real-world** (not synthetically rendered) single-image deraining benchmark. Rain/clean pairs were "automatically generated from a large number of real-world rainy video clips through a simple yet effective rain density-controllable filtering method" (per the paper), yielding two parallel tracks at different rain densities: | |
| - **RealRain-1k-H** (Heavy): denser, more visually severe rain streaks. | |
| - **RealRain-1k-L** (Light): the same underlying scene pool, filtered at a lower rain-density threshold. | |
| Both tracks share the same per-scene numeric IDs, high resolution (**variable** — not fixed-size patches; images range from roughly 550×550 up to 1920×1080 in this copy), background diversity, and strict spatial alignment between rain and clean frames. The authors also released a companion **synthetic** dataset, SynRain-13k, generated by extracting the real rain-streak layers and compositing them onto natural images — **not included in this mirror**, which covers RealRain-1k only. | |
| --- | |
| # Changes from the Official Release | |
| - **Removed 9 `.DS_Store` files** (macOS Finder metadata, present in every split of the source copy — not part of the dataset). | |
| - **Removed 1 duplicate file**: `RealRain-1k-L/test/target/1101 [conflicted].png`, a byte-identical duplicate of `1101.png` left behind by a cloud-sync conflict (Dropbox/OneDrive-style naming) in the source copy. `1101.png` itself is untouched and present. | |
| - No images added, modified, or otherwise removed. No relabeling. No further subsampling. | |
| - Directory layout preserved exactly as received (`{H,L}/{train,validation,test}/{input,target}`). | |
| --- | |
| # Dataset Structure | |
| ```text | |
| realrain-1k/ | |
| ├── README.md | |
| ├── banner.jpg | |
| ├── RealRain-1k-H/ | |
| │ ├── train/{input,target}/ # 784 pairs | |
| │ ├── validation/{input,target}/ # 112 pairs | |
| │ └── test/{input,target}/ # 224 pairs | |
| └── RealRain-1k-L/ | |
| ├── train/{input,target}/ # 784 pairs | |
| ├── validation/{input,target}/ # 112 pairs | |
| └── test/{input,target}/ # 224 pairs | |
| ``` | |
| `input`/`target` filenames share identical stems (e.g. `input/104.png` ↔ `target/104.png`), so this mirror works directly with a generic paired-image loader — no custom filename-matching logic required (unlike SPA-Data's `rain-{id}`/`norain-{id}` prefix mismatch). | |
| | Track | Split | Pairs | Size on disk | | |
| |---|---|---|---| | |
| | H (Heavy) | train / validation / test | 784 / 112 / 224 | ~2.4 GB | | |
| | L (Light) | train / validation / test | 784 / 112 / 224 | ~2.3 GB | | |
| | **Total** | — | **2,240** | **~4.6 GB** | | |
| --- | |
| # Usage with ClearView | |
| [ClearView](https://github.com/dronefreak/clearview) doesn't need a dedicated parser class for this dataset — since `input`/`target` filenames match exactly, the generic `ImagePairDataset` handles it directly: | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| from clearview.data import ImagePairDataset, get_train_transforms, get_val_transforms | |
| data_dir = snapshot_download(repo_id="dronefreak/RealRain-1k", repo_type="dataset") | |
| # Pick a track: RealRain-1k-H (heavy) or RealRain-1k-L (light) | |
| track = f"{data_dir}/RealRain-1k-H" | |
| train_ds = ImagePairDataset( | |
| rainy_dir=f"{track}/train/input", | |
| clean_dir=f"{track}/train/target", | |
| transform=get_train_transforms(crop_size=(256, 256)), | |
| ) | |
| val_ds = ImagePairDataset( | |
| rainy_dir=f"{track}/validation/input", | |
| clean_dir=f"{track}/validation/target", | |
| transform=get_val_transforms(), | |
| ) | |
| rainy, clean = train_ds[0] | |
| ``` | |
| Or directly via the training CLI (`--dataset-type pair` with explicit path overrides): | |
| ```bash | |
| clearview-train \ | |
| --data-dir <path-to-downloaded-snapshot>/RealRain-1k-H \ | |
| --dataset-type pair \ | |
| --train-rainy train/input --train-clean train/target \ | |
| --val-rainy validation/input --val-clean validation/target \ | |
| --model restormer --batch-size 8 --crop-size 256 --epochs 100 \ | |
| --output-dir ./runs/realrain1k_h_restormer | |
| ``` | |
| Swap `RealRain-1k-H` → `RealRain-1k-L` for the light-rain track. Evaluate against the held-out `test` split the same way via `clearview-evaluate --dataset-type pair --rainy-dir test/input --clean-dir test/target`. | |
| --- | |
| # Dataset Sources | |
| ## Original Paper | |
| **Toward Real-world Single Image Deraining: A New Benchmark and Beyond** | |
| Wei Li, Qiming Zhang, Jing Zhang, Zhen Huang, Xinmei Tian, Dacheng Tao | |
| arXiv preprint, 2022. | |
| - **arXiv:** https://arxiv.org/abs/2206.05514 | |
| ## Official Resources | |
| - **Code + Dataset:** https://github.com/hiker-lw/RealRain-1k | |
| - **OneDrive:** https://1drv.ms/u/s!AimBgYV7JjTlgg1MmR2tfBPW1Egh?e=rUNw3m | |
| - **Google Drive:** https://drive.google.com/drive/folders/1rk7jdBZifNe_OKJ6j-0Ne8gjYYIg6Qc5 | |
| --- | |
| # Attribution | |
| **All credit for the dataset belongs entirely to the original authors: Wei Li, Qiming Zhang, Jing Zhang, Zhen Huang, Xinmei Tian, and Dacheng Tao.** | |
| If you use this dataset in your research, **please cite the original publication below.** | |
| --- | |
| # License | |
| The official RealRain-1k repository ships an explicit **MIT License**, the most permissive of the licenses found across the datasets in this mirror collection — it allows unrestricted use, modification, and redistribution (including commercial), provided the copyright notice is retained. | |
| This repository is distributed under the same terms: **MIT**. | |
| --- | |
| # Citation | |
| If you use this dataset, please cite: | |
| ```bibtex | |
| @article{li2022toward, | |
| title={Toward Real-world Single Image Deraining: A New Benchmark and Beyond}, | |
| author={Li, Wei and Zhang, Qiming and Zhang, Jing and Huang, Zhen and Tian, Xinmei and Tao, Dacheng}, | |
| journal={arXiv preprint arXiv:2206.05514}, | |
| year={2022} | |
| } | |
| ``` | |
| --- | |
| # Acknowledgements | |
| We sincerely thank Wei Li, Qiming Zhang, Jing Zhang, Zhen Huang, Xinmei Tian, and Dacheng Tao for creating and publicly releasing this valuable real-world deraining benchmark. | |