Datasets:
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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
```
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