A Bayesian galaxy morphology classifier: a galaxy-pretrained ConvNeXt-nano encoder (Zoobot) finetuned with a Dirichlet-Multinomial output head over the 10-question / 34-answer Galaxy Zoo DESI decision tree. Produces analytic per-answer credible intervals; tested at ≥ 0.93 coverage at the 95% level after post-hoc temperature scaling.

Live demo

Hosted at https://roth1414-galaxy-vit-demo.hf.space (7 tabs):

Classify — upload → top-3 + GradCAM; "Compare with M3" runs the Dirichlet head on the same image side-by-side.
Posteriors — per-question Dirichlet bars with 95% CI whiskers, Sankey question-tree view, per-question GradCAM dropdown, and the "most interesting galaxies" outlier gallery (entropy / BALD / model-vs-volunteer disagreement).
Explorer — 2-D / 3-D UMAP toggle over 2,462 DR8 test galaxies; lasso, hover thumbnails crossfade to precomputed GradCAM, click for posterior.
Sky — RA/Dec scatter of ~14k joined-catalog galaxies, Aladin Lite DR10 embed, and an object-name resolver (M31, NGC 1300, …) via CDS Sesame.
Similar — cosine-kNN against the cached ConvNeXt features; query by upload or cache idx.
Training — animation of 24 demo galaxies' positions in feature space across all training epochs (final-epoch UMAP fit projected onto every earlier epoch so the cloud doesn't jitter).
Model Card — this content, plus curves, sample failure cases, and the interpretability gallery.

A 90-second walkthrough is in docs/loom_shotlist.md in the source repo. The complete v2 changelog is at CHANGELOG.md.

Model summary


Architecture	ConvNeXt-nano (640-D feature) + `Linear(640, 34)` + `softplus + α_floor`
Parameters	~16M (Zoobot encoder) + 21,794 (head)
Input	RGB DECaLS cutout, 224×224
Output	34-D concentration vector α (positive); per-question Dirichlet posterior
Training data	80 train + 20 val tar shards of `mwalmsley/gz_desi_wds`, DR8 subset only
Optimizer	AdamW; encoder LR 1e-5, head LR 1e-3, weight decay 0.05
Schedule	Cosine; 3-epoch warmup; 2 head-only epochs before encoder unfreeze
Precision	bf16 forward; `gammaln` and loss in fp32
Early stop	`val/vote_mae_macro` patience 5; converged at epoch 25 of 50

Metrics (held-out DR8 test split)

Metric	Raw	T-calibrated (T=60)
Macro vote-fraction MAE	0.0883	0.0883 (preserved by design)
Macro coverage at 95% CI	0.49	0.93
`all_finite` α throughout training	Yes	—
Monotone train loss, epochs 1–5	Yes	—

For the cross-entropy baseline trained on the same data (configs/m2_w23_reproduction.yaml), macro top-1 = 0.8445 at the per-question plurality, comparable to Walmsley+23's published numbers.

Per-question breakdown (Dirichlet, calibrated test set)

Question	MAE	Coverage
smooth-or-featured	0.064	≥ 0.90
disk-edge-on	0.092	≥ 0.90
has-spiral-arms	0.124	≥ 0.85
bar	0.102	≥ 0.90
bulge-size	0.089	≥ 0.85
how-rounded	0.067	≥ 0.90
edge-on-bulge	0.099	≥ 0.85
spiral-winding	0.119	≥ 0.85
spiral-arm-count	0.077	≥ 0.85
merging	0.051	≥ 0.95

Lower-data questions (spiral-winding, spiral-arm-count, edge-on-bulge) have the wider per-question CIs and the highest MAE; this is the expected pattern when volunteer disagreement is large per-galaxy.

Science validation (T5.2)

On the volunteer-cross-matched subset (~4,400 galaxies), model-predicted any-bar fraction recovers the canonical bar–bulge anti-correlation:

Spearman ρ vs bulge ordinal: model +0.198, volunteer +0.146, both p < 10⁻²².
Same sign, same monotone direction, model slightly stronger than the noisier volunteer estimator.

How to use

python

import torch
from huggingface_hub import hf_hub_download
from PIL import Image

# Download the weights.
ckpt_path = hf_hub_download(
    repo_id="jroth1414/galaxy-vit-zoobot-dirichlet",
    filename="best.pt",
)

# Construct the model (needs the project source for the head wrapper).
from galaxy_vit.models.dirichlet_head import build_zoobot_dirichlet
from galaxy_vit.data.transforms import build_eval_transform, load_normalization

model, _, _ = build_zoobot_dirichlet(num_answers=34, alpha_floor=1.0)
ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True)
model.load_state_dict(ckpt["model_state_dict"])
model.eval()

# Predict on a single image.
mean, std = load_normalization("configs/normalization.json")
tf = build_eval_transform(image_size=224, mean=mean, std=std)
img = Image.open("your_galaxy.jpg").convert("RGB")
with torch.no_grad():
    alpha = model(pixel_values=tf(img).unsqueeze(0)).alpha

# alpha has shape (1, 34) in canonical question_index_groups order.
# Posterior mean for the smooth-or-featured 3-answer block:
print((alpha[0, :3] / alpha[0, :3].sum()).tolist())

For analytic 95% credible intervals on every answer, see galaxy_vit.inference.posterior.credible_interval in the source repo.

Calibration regime

The released best.pt is the raw Dirichlet checkpoint (no temperature applied at save time). The temperature scalar that recovers coverage = 0.93 at 95% CI is shipped alongside in calibrated_metrics.json (single-T value, default 60.0). Two ways to use it:

For per-image discriminability (live demo, model card visualizations): use raw α. Per-galaxy CIs visibly differ between galaxies.
For calibrated coverage (population statistics, science cases): divide α by T before computing posteriors. The posterior mean is unchanged (scale invariance).

The release script's default is raw; opt in to calibrated with scripts/calibrate_dirichlet.py.

Intended use

Research-grade galaxy morphology classification on DECaLS-style cutouts. The model was trained only on the Walmsley+23 DR8 subset; predictions on substantially out-of-distribution imagery (HSC, JWST, deep-field) should be re-validated.

Limitations and biases

Distribution match: training images are DECaLS DR8 at ~3-color compositing. Models exhibit known sensitivity to background subtraction, PSF, and pixel scale.
Galaxy Zoo volunteer labels are noisy: typical inter-volunteer agreement on fine questions (spiral-winding, spiral-arm-count) is well below 80%; the practical accuracy ceiling on those questions is similar.
Imbalanced class support: the labeled training subset is dominated by smooth galaxies (~80%). The model is well-calibrated on this prior; deploying on a population with a different morphology prior will need re-calibration.
Decision-tree gating: per-question predictions for disk-edge-on, has-spiral-arms, bar, bulge-size, how-rounded, edge-on-bulge, spiral-winding, spiral-arm-count are only meaningful for galaxies whose upstream classifications match the gating answers. The active flag in the serving response handles this for downstream consumers.
bf16 numerical caveat: the model trains in bf16 but gammaln and the loss run in fp32. Don't try to inference the head's loss in pure bf16; the head's forward in bf16 is fine.

Training data

The labeled DR8 subset of the GZ DESI WebDataset (mwalmsley/gz_desi_wds): 80 train shards, 20 val shards, 20 held-out test shards, totaling ~80k labeled galaxies after the has_any_dr8_votes filter. Vote counts follow the canonical Walmsley+23 schema; the project schema module (galaxy_vit.data.schema) pins answer order against upstream galaxy-datasets and asserts canonical-ordering parity at every checkpoint load.

Companion release

Per-galaxy Dirichlet predictions for the 61,440-row DR8 inference set are published as the dataset jroth1414/galaxy-vit-gz-desi-dirichlet-predictions. That dataset's key and dr8_id columns let downstream users cross-match the predictions against the Walmsley+23 volunteer catalog.

Source code

github.com/jroth1414/galaxy-vit — full training, inference, serving, and frontend code. The exact commit that produced best.pt is recorded in runs/m3_dirichlet/run_config.json.

Citation

bibtex

@misc{galaxy_vit_zoobot_dirichlet_v1,
  author = {Roth, John},
  title  = {Galaxy-ViT: Bayesian multi-question galaxy morphology with Zoobot + Dirichlet},
  year   = {2026},
  url    = {https://huggingface.co/jroth1414/galaxy-vit-zoobot-dirichlet},
}

Built on Walmsley+22 (Zoobot foundation model), Walmsley+23 (Galaxy Zoo DESI), and the galaxy-datasets canonical schema.

License

Model weights: CC-BY-4.0 (matches Zoobot upstream). Underlying training data retains its original Galaxy Zoo / DECaLS licenses.