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---
annotations_creators:
- expert-generated
language: []
language_creators: []
license:
- cc-by-nc-4.0
multilinguality: []
pretty_name: 'TD01: Natural Ground Texture Photos'
size_categories:
- n<1K
source_datasets:
- original
tags:
- texture-synthesis
- photography
- non-infringing
task_categories:
- unconditional-image-generation
task_ids: []
viewer: false
---
_The Dataset Teaser is now enabled instead! Isn't this better?_
# TD 01: Natural Ground Textures
This dataset contains multi-photo texture captures in outdoor nature scenes — all focusing on the ground. Each set has different photos that showcase texture variety, making them ideal for training a domain-specific image generator!
Overall information about this dataset:
* **Format** — JPEG-XL, lossless RGB
* **Resolution** — 4032 × 2268
* **Device** — mobile camera
* **Technique** — hand-held
* **Orientation** — portrait or landscape
* **Author**: Alex J. Champandard
* **Configurations**: 4K, 2K (default), 1K
To load the medium- and high-resolution images of the dataset, you'll need to install `jxlpy` from [PyPI](https://pypi.org/project/jxlpy/) with `pip install jxlpy`:
```python
# Recommended use, JXL at high-quality.
from jxlpy import JXLImagePlugin
from datasets import load_dataset
d = load_dataset('texturedesign/td01_natural-ground-textures', 'JXL@4K')
print(len(d['train']), len(d['test']))
```
The lowest-resolution images are available as PNG with a regular installation of `pillow`:
```python
# Alternative use, PNG at low-quality.
from datasets import load_dataset
d = load_dataset('texturedesign/td01_natural-ground-textures', 'PNG@1K')
# EXAMPLE: Discard all other sets except Set #1.
dataset = dataset.filter(lambda s: s['set'] == 1)
# EXAMPLE: Only keep images with index 0 and 2.
dataset = dataset.select([0, 2])
```
Use built-in dataset `filter()` and `select()` to narrow down the loaded dataset for training, or to ease with development.
## Set #1: Rock and Gravel
* **Description**:
- surface rocks with gravel and coarse sand
- strong sunlight from the left, sharp shadows
* **Number of Photos**:
- 7 train
- 2 test
* **Edits**:
- rotated photos to align sunlight
- removed infrequent objects
* **Size**: 77.8 Mb
## Set #2: Dry Grass with Pine Needles
* **Description**:
- field of dry grass and pine needles
- sunlight from the top right, some shadows
* **Number of Photos**:
- 6 train
- 1 test
* **Edits**:
- removed dry leaves and large plants
- removed sticks, rocks and sporadic daisies
* **Size**: 95.2 Mb
## Set #3: Chipped Stones, Broken Leaves and Twiglets
* **Description**:
- autumn path with chipped stones and dry broken leaves
- diffuse light on a cloudy day, very soft shadows
* **Number of Photos**:
- 9 train
- 3 test
* **Edits**:
- removed anything that looks green, fresh leaves
- removed long sticks and large/odd stones
* **Size**: 126.9 Mb
## Set #4: Grass Clumps and Cracked Dirt
* **Description**:
- clumps of green grass, clover and patches of cracked dirt
- diffuse light on cloudy day, shadows under large blades of grass
* **Number of Photos**:
- 9 train
- 2 test
* **Edits**:
- removed dry leaves, sporadic dandelions, and large objects
- histogram matching for two of the photos so the colors look similar
* **Size**: 126.8 Mb
## Set #5: Dirt, Stones, Rock, Twigs...
* **Description**:
- intricate micro-scene with grey dirt, surface rock, stones, twigs and organic debris
- diffuse light on cloudy day, soft shadows around the larger objects
* **Number of Photos**:
- 9 train
- 3 test
* **Edits**:
- removed odd objects that felt out-of-distribution
* **Size**: 102.1 Mb
## Set #6: Plants with Flowers on Dry Leaves
* **Description**:
- leafy plants with white flowers on a bed of dry brown leaves
- soft diffuse light, shaded areas under the plants
* **Number of Photos**:
- 9 train
- 2 test
* **Edits**:
- none yet, inpainting doesn't work well enough
- would remove long sticks and pieces of wood
* **Size**: 105.1 Mb
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