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Low-Light Restoration Dataset

Paired low-light / normal-light image patches for training and evaluation.

Contents of low-light.tar

low-light/
├── train/ 30,000 paired patches (60,000 files)
├── val/ 1,000 paired patches (2,000 files)
├── test/ 1,000 input patches (no ground truth)
└── dataset.py reference PyTorch Dataset (Python 3.10+)

All images are lossless WebP (.webp).

File naming

Every image is named <id>-<role>.webp where role ∈ {in, gt}:

<id> is opaque; do not parse it. Pairing is by exact stem match.

Quick start

from pathlib import Path
from torch.utils.data import DataLoader
from dataset import (
 PairedLowLightDataset, TestLowLightDataset,
 PairedCompose, PairedRandomCrop, PairedRandomFlip, PairedToTensor,
)

root = Path("low-light")

train_tf = PairedCompose([
 PairedRandomCrop(256),
 PairedRandomFlip(p_h=0.5),
 PairedToTensor(),
])

train_set = PairedLowLightDataset(root / "train", transform=train_tf)
val_set = PairedLowLightDataset(root / "val", transform=None)
test_set = TestLowLightDataset(root / "test", transform=None)

train_loader = DataLoader(train_set, batch_size=16, shuffle=True, num_workers=4)
val_loader = DataLoader(val_set, batch_size=1, shuffle=False, num_workers=2)
test_loader = DataLoader(test_set, batch_size=1, shuffle=False, num_workers=2)

for x, y in train_loader: # x, y are float32 CHW tensors in [0, 1]
 ...

for x, stem in test_loader: # test yields (input, stem)
 pred = model(x)
 save_image(pred, f"submission/{stem[0]}-in.webp")

Dataset classes (in dataset.py)

PairedLowLightDataset(root, transform=None)

For train/ and val/. Returns (input_tensor, gt_tensor).

TestLowLightDataset(root, transform=None)

For test/. Returns (input_tensor, stem) where stem lets you save predictions under the original filename.

Transform contract

A transform may be one of:

1. None — images are converted to float32 CHW tensors in [0, 1].
2. A single-image torchvision-style callable fn(pil) -> tensor — applied independently to input and GT. Use only for deterministic ops (ToTensor, Normalize). Random single-image transforms will desync the pair.
3. A pair-aware callable fn(in_pil, gt_pil) -> (in_tensor, gt_tensor), marked by setting fn.paired = True. The callable owns randomness and must apply the same geometric augmentation to both images.

The provided PairedCompose, PairedRandomCrop, PairedRandomFlip, PairedToTensor building blocks already follow contract #3.

Submission format

For each <id>-in.webp in test/, produce a restored image and save it as <id>-in.webp (or .png if preferred). Keep the original stem.

Evaluation pairs each prediction against the private ground truth held by the organizers — do not attempt to obtain or infer test GTs.

Requirements

• Python 3.10+
• torch, torchvision, Pillow (with WebP support; built into modern Pillow)