README.md

---
license: cc0-1.0
task_categories:
- image-classification
tags:
- image
- synthetic
- imagefolder
size_categories:
- n<1K
configs:
- config_name: full
  default: true
  metadata_filenames:
  - full_train.csv
  - full_val.csv
  - full_test.csv
  data_files:
  - split: train
    path:
    - images/*.webp
    - full_train.csv
  - split: validation
    path:
    - images/*.webp
    - full_val.csv
  - split: test
    path:
    - images/*.webp
    - full_test.csv
- config_name: core
  metadata_filenames:
  - core_train.csv
  - core_val.csv
  - core_test.csv
  data_files:
  - split: train
    path:
    - images/*.webp
    - core_train.csv
  - split: validation
    path:
    - images/*.webp
    - core_val.csv
  - split: test
    path:
    - images/*.webp
    - core_test.csv
---

# Tiny overlapping ImageFolder demo

A tiny synthetic repository for testing Hugging Face Dataset Viewer.

- 12 lossless WebP images, each exactly 128×128.
- `full`: 8 train, 2 validation, 2 test.
- `core`: 4 train, 1 validation, 1 test.
- `core` is a subset of `full`.
- Images exist once in `images/`.
- `manifest.csv` is the canonical manifest.
- `splits/` contains lightweight source indices.
- Root `full_*.csv` and `core_*.csv` are materialized metadata files used by ImageFolder.

## Expected Viewer

The Viewer should show:

- subsets: `full`, `core`
- splits: `train`, `validation`, `test`
- an `image` column with thumbnails
- metadata such as `shape`, `background`, and `caption`

## Load

```python
from datasets import load_dataset

full = load_dataset("YOUR_USERNAME/YOUR_DATASET", "full")
core = load_dataset("YOUR_USERNAME/YOUR_DATASET", "core")
```

## Upload

Run from this directory:

```bash
pip install -U huggingface_hub
hf auth login
hf upload YOUR_USERNAME/YOUR_DATASET . . --repo-type=dataset
```