| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| language: |
| - en |
| tags: |
| - plant-disease |
| - test-fixture |
| - debug-grade |
| size_categories: |
| - n<1K |
| pretty_name: PlantDoc (tiny) |
| --- |
| |
| # PlantDoc — tiny variant |
|
|
| > **Warning: this is a debug-grade subset, not a faithful subsample for analysis.** Use it for test suites, smoke tests, and notebook iteration. For substantive work, use [`geraldmc/plantdoc-full`](https://huggingface.co/datasets/geraldmc/plantdoc-full). |
|
|
| A 164-image stratified subsample of [`geraldmc/plantdoc-full`](https://huggingface.co/datasets/geraldmc/plantdoc-full), built to support fast iteration. Loading is roughly a 50 MB download instead of ~950 MB, and a pass over the dataset takes seconds rather than minutes. |
|
|
| ## Quick start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("geraldmc/plantdoc-tiny", revision="v0.1.0", split="train") |
| print(len(ds)) # 164 |
| ``` |
|
|
| Inside the iResearch Institute 2026 Virtual Lab: |
|
|
| ```python |
| import irilab2026 as iri |
| |
| metadata_df, hf_dataset = iri.load_plantdoc(variant="tiny") |
| ``` |
|
|
| ## What's in this dataset |
|
|
| - **164 images** stratified across 28 classes, 83 train + 81 test |
| - **Schema identical to `plantdoc-full`** — same 8 columns (image + 7 metadata), same values for any image that appears in both |
| - **Subsample rule:** `min(3, available)` per class per split, with a fixed seed |
|
|
| For every non-orphan class, this means 3 train + 3 test = 6 images. The orphan class (`Tomato two spotted spider mites leaf`) appears with its 2 train images and 0 test images, exactly as in the full variant. |
|
|
| ## Schema, normalization, caveats |
|
|
| See the **[full variant's dataset card](https://huggingface.co/datasets/geraldmc/plantdoc-full)** for: |
|
|
| - Column descriptions and types |
| - Normalization rules for `host` and `disease` |
| - The orphan-class caveat |
| - Filename quirks |
| - Citation and license details |
|
|
| The schema is identical between the two variants; duplicating it here invites drift. |
|
|
| ## Subsample parameters |
|
|
| - **Per-class budget:** `min(3, available)` per (class, split) |
| - **Seed:** `42` (passed as `random_state` to pandas `.sample()`) |
| - **Source:** `geraldmc/plantdoc-full` at revision `v0.1.0` |
|
|
| The subsample is fully deterministic given these three parameters. Re-running the build script with the same source and seed produces byte-identical metadata. Image bytes also match exactly because the same upstream files are referenced. |
|
|
| ## What this dataset is NOT for |
|
|
| - **Training a classifier.** 3 training images per class is far too few to learn anything; this isn't a "small training set" — it's a fixture for code paths. |
| - **Measuring per-class accuracy.** Per-class test set sizes here are 1–3 images; any "accuracy" you compute is dominated by sampling noise. |
| - **Comparing model architectures.** Any signal would be noise at this scale. |
| - **Reporting results in a paper.** Whatever you find on the tiny variant is not a finding about PlantDoc — it's a finding about 164 images that happen to live under this repo ID. |
|
|
| ## What this dataset IS for |
|
|
| - **Test suite use.** Fast assertion that loader code, Dataset wrappers, and training pipelines run end-to-end without surprise. |
| - **Notebook iteration.** Debugging a preprocessing pipeline or augmentation strategy without paying for the full dataset's load time. |
| - **CI runs.** Tests that exercise loader behavior can use this variant on every push without burning minutes on downloads. |
|
|
| ## License |
|
|
| CC BY 4.0, matching the full variant and the upstream dataset. |
|
|
| ## Citation |
|
|
| Cite the upstream dataset (Singh et al. 2020) and reference this tiny variant as `geraldmc/plantdoc-tiny @ v0.1.0`. Full BibTeX in the full variant's dataset card. |
