KBlueLeaf/low-light-project

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}:

Split	Files
train/	<id>-in.webp paired with <id>-gt.webp (30,000 pairs)
val/	<id>-in.webp paired with <id>-gt.webp (1,000 pairs)
test/	<id>-in.webp only — no GT is provided

<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)