---
# license below = the dataset COMPILATION/curation/metadata (CC0, matching imageomics/TreeOfLife-200M). Individual IMAGES retain their own per-image licenses, recorded per row (license_name / license_link / copyright_owner) from TreeOfLife-200M provenance.parquet. See the Licensing section.
license: cc0-1.0
tags:
  - biology
  - imageomics
  - fine-grained-classification
  - bioclip
  - lance
pretty_name: Fine-Grained Challenges
---

# Fine-Grained Challenges

Groups of morphologically similar species for evaluating and tuning fine-grained classifiers on top of [BioCLIP ecosystem model](https://imageomics.github.io/bioclip-ecosystem/pages/models.html) embeddings. Each group gathers species that are easily confused with one another, and the groups span several clades so a classifier can be probed on the distinctions that actually matter rather than on coarse taxonomy.

The corpus lives in a [Lance dataset](https://huggingface.co/docs/hub/en/datasets-lance) and can be acted on as a whole, on any single group independently, or on any single image. Images, embeddings from three BioCLIP model generations, taxonomic lineage, provenance, and search indexes all live in the same table:

```
data/challenges.lance/
```

## Schema

| Column | Type | Meaning |
|---|---|---|
| `uuid` | string | stable image id |
| `challenge_group` | string | which group, e.g. `Peromyscus`, `Trochilidae`, `Ixodidae`, `zebra` |
| `seen_in_training` | list\<string\> | model-version ids whose training data included this image, e.g. `["bioclip-2", "bioclip-2.5"]`; `[]` = held out from every listed model (see Considerations) |
| `species` | string | full binomial (`Peromyscus maniculatus`) if available, else null |
| `kingdom`…`genus`, `scientific_name` | string | taxonomy |
| `common_name` | string | common name if available, else null |
| `source_dataset`, `source_id`, `publisher`, `basisOfRecord`, `img_type`, `resolution_status` | string | record provenance |
| `source_url` | string | URL of the image (from TreeOfLife-200M provenance) |
| `license_name`, `license_link`, `copyright_owner` | string | per-image license + attribution (see Licensing) |
| `emb_bioclip` | fixed_size_list\<float32\>[512] | original BioCLIP embedding (IVF_PQ-indexed) |
| `emb_bioclip2` | fixed_size_list\<float32\>[768] | BioCLIP 2 embedding (IVF_PQ-indexed) |
| `emb_bioclip2p5` | fixed_size_list\<float32\>[1024] | BioCLIP 2.5 ViT-H/14 embedding (IVF_PQ-indexed) |
| `image` | binary | webp bytes (decode with `PIL.Image.open(io.BytesIO(...))`) |

`seen_in_training` is per-image and is constant within a source cohort, so a single group can mix images seen in training with held-out images. See Considerations for using this data.

Embedding columns contain raw, unnormalized encoder output stored as `float32`. Normalize downstream when using cosine similarity or when an analysis requires unit-length features. Model repository IDs, revisions, dimensions, preprocessing, and software versions are recorded in `data/challenges.embedding_models.json` and in each embedding field's Arrow metadata.

## Groups

Each group is a set of confusable species drawn from [TreeOfLife-200M](https://huggingface.co/datasets/imageomics/TreeOfLife-200M). Images are sampled at up to 100 per species with a fixed random seed, so classes stay roughly balanced; images that resolve only to a genus are kept as a separate bucket, also capped at 100.

| group | contents |
|---|---|
| `Peromyscus` | deermice and relatives, plus a genus-only camera-trap cohort |
| `Trochilidae` | hummingbirds |
| `Ixodidae` | hard ticks |
| `zebra` | the zebras of genus Equus (`Equus quagga`, `zebra`, `grevyi`, `hartmannae`) |

The species-labeled groups are drawn from TreeOfLife-200M. The `Peromyscus`
group also includes 305 genus-only camera-trap images from WCS Camera Traps
and NACTI. MegaDetector v1000 Redwood retained frames with an animal detection
confidence of at least 0.30. These camera-trap rows have null `species` values
and `seen_in_training = []`; they are intended for open-ended prediction and
inspection, not species-level accuracy measurement.

## Usage

Install: `uv pip install pylance polars pillow`

```python
import lance
import polars as pl
URI = "hf://datasets/thompsonmj/test-fine-grained-challenges/data/challenges.lance"
ds = lance.dataset(URI) # scans remotely over hf://, no full download

# Groups present and their sizes
pl.from_arrow(ds.to_table(columns=["challenge_group"]))["challenge_group"].value_counts()

# One group independently (filter pushed down to only retrieve relevant rows and column projection to only retrieve relevant columns)
hummingbirds = ds.scanner(
    filter="challenge_group = 'Trochilidae'",
    columns=["uuid", "species", "emb_bioclip2p5"],
).to_table()

# Select by training exposure (unseen test set vs. seen in training)
unseen = ds.scanner(filter="array_length(seen_in_training) = 0").to_table()
seen_by_bioclip2 = ds.scanner(
    filter="array_has(seen_in_training, 'bioclip-2')").to_table()

# Image bytes (raw webp), same pattern as the HF Lance image example
import io
from PIL import Image
row = ds.take([0], columns=["image", "species"]).to_pylist()[0]
img = Image.open(io.BytesIO(row["image"]))

# Materialize (i.e. download) a group locally for heavy/training use (avoids Hub rate limits)
lance.write_dataset(hummingbirds, "./Trochilidae.lance")
```

Because reads are columnar and pushed down over `hf://`, you can browse metadata cheaply by projecting to just the columns you need (e.g. omit `image` and the `emb_*` columns). For example, count the images of one species without fetching any images or embeddings:

```python
ds.count_rows(filter="species = 'Peromyscus maniculatus'")
```

## Considerations for using this data

The `seen_in_training` field records the model versions whose training data included each image, so in-distribution evaluation can be separated from unseen tests:

| cohort | seen_in_training | meaning |
|---|---|---|
| the groups here (via TreeOfLife-200M) | `["bioclip-2", "bioclip-2.5"]` | in the training corpus of both models |
| LILA camera-trap cohort | `[]` | novel to every listed model |

For TreeOfLife-200M rows, `seen_in_training` is derived per image from its
presence in the training data for the listed model versions. The LILA
camera-trap images were sourced separately and are marked unseen. They have
only a genus label, so use them for prediction inspection rather than
species-level evaluation.

Models referenced: [`bioclip`](https://huggingface.co/imageomics/bioclip), [`bioclip-2`](https://huggingface.co/imageomics/bioclip-2), and [`bioclip-2.5-vith14`](https://huggingface.co/imageomics/bioclip-2.5-vith14). The corresponding columns are `emb_bioclip`, `emb_bioclip2`, and `emb_bioclip2p5`.

## Licensing and attribution

The dataset compilation (curation, metadata, embeddings, and indexes) is released under CC0-1.0, matching `imageomics/TreeOfLife-200M`. This does not relicense the images.

Each image keeps its own license, recorded in its row (`license_name`, `license_link`, `copyright_owner`) from TreeOfLife-200M's `provenance.parquet`.

No-derivatives (`*-nd`) licenses are excluded at build time, since the stored webp and embeddings are derivative works. Images whose license cannot be determined from provenance are excluded for the same reason. The remaining mix is mostly NonCommercial (`*-nc-*`), with some ShareAlike (`*-sa-*`), plain attribution, and public-domain images.

Use each image under its own license.

Per-license counts, computed from the data (`URI` as defined above):

```python
import lance
import polars as pl
t = lance.dataset(URI).to_table(columns=["challenge_group", "license_name"])
pl.from_arrow(t).group_by(["challenge_group", "license_name"]).len()  # license counts, per group
```