--- 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 RealRain-1k sample heavy/light rain and clean triples

![Task](https://img.shields.io/badge/Task-Image%20Restoration-blue?style=flat-square) ![Domain](https://img.shields.io/badge/Domain-Real--World%20Rain-0aa1a7?style=flat-square) ![Dataset](https://img.shields.io/badge/Dataset-RealRain--1k-orange?style=flat-square) ![Pairs](https://img.shields.io/badge/Pairs-2%2C240-success?style=flat-square) ![Splits](https://img.shields.io/badge/Splits-Train%20%7C%20Val%20%7C%20Test-blueviolet?style=flat-square) ![License](https://img.shields.io/badge/License-MIT-lightgrey?style=flat-square) > **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 /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.