dinov2-base-imagenet1k-1-layer-galaxy10-decals-full-v2

facebook/dinov2-base-imagenet1k-1-layer adapted to galaxy morphology classification on Galaxy10 DECaLS (10 classes) by full fine-tuning (all parameters updated), under the revised protocol of the paper When foundation models meet astronomical data (Astronomy and Computing, revised version, 2026). One of the 80 configurations (40 pretrained backbones x two adaptation regimes) of the collection Vision Foundation Models Benchmark.

Results (held-out test set, 1,774 images)

	Accuracy (%)	Weighted F1 (%)
This checkpoint (seed 42, epoch 6 of 30)	83.54	83.33
Mean +/- s.d. over three seeds (42, 1, 2)	84.80 +/- 1.24	84.56 +/- 1.21

Scores are computed at the epoch of minimum validation loss. The per-epoch metrics of all runs, the tables of the paper and the data split are in the dataset vision-foundation-models-benchmark-runs.

Training protocol

Data: Galaxy10 DECaLS (17,736 images). The public test split (1,774 images) is the held-out test set; the public train split is divided, with stratification by class, into 14,188 training and 1,774 validation images (indices in the dataset repository above). The validation set is used only to select the epoch.
Recipe (identical for the 40 backbones): AdamW (betas 0.9/0.999, eps 1e-8, weight decay 0.1, gradient clipping 1.0), learning rate 5e-5, linear schedule with 10% warm-up, batches of 32 images with gradient accumulation over 8 steps (effective batch 256), 30 epochs, single precision (fp32, TF32 disabled), one GPU. Augmentation: random resized crop to the input resolution and random horizontal flip. Evaluation: resize and centre crop to 256x256 px, normalisation of the checkpoint's image processor. Seed 42 (seeds 1 and 2 give the mean and s.d. above).
Regime: full fine-tuning (all parameters updated). The head is a single linear layer with ten outputs.
Selection: epoch of minimum validation loss (epoch 6 here); the weights of this repository are those of that epoch.
Software: PyTorch 2.3.0, transformers 4.37.2, datasets 2.19.1, torchvision 0.18.0.

Per-epoch metrics (seed 42)

Epoch	Val. loss	Val. acc.	Test acc.	Test weighted F1
1	0.6549	78.58	78.35	78.12
2	0.6170	78.47	81.17	80.74
3	0.6233	79.26	78.86	78.60
4	0.6601	79.20	79.26	78.63
5	0.4953	83.43	83.37	83.12
6 (selected)	0.4807	84.27	83.54	83.33
7	0.5134	83.71	82.41	82.25
8	0.4893	84.10	83.03	82.81
9	0.4953	84.16	84.33	83.89
10	0.4869	84.50	83.26	82.82
11	0.5152	82.98	83.71	83.71
12	0.5807	82.02	81.91	81.55
13	0.5118	84.33	85.79	85.67
14	0.5225	85.17	83.48	83.43
15	0.5109	85.40	84.39	84.08
16	0.5002	85.40	84.72	84.39
17	0.4815	85.57	85.29	84.93
18	0.5280	84.61	84.95	84.88
19	0.5416	84.78	84.27	84.31
20	0.5324	85.96	85.63	85.46
21	0.5133	86.30	85.46	85.37
22	0.5878	85.91	84.95	84.95
23	0.5483	86.30	85.17	84.91
24	0.5965	85.46	85.34	85.37
25	0.6061	86.41	86.25	85.99
26	0.5754	87.09	86.02	85.90
27	0.5994	86.64	85.96	85.76
28	0.5710	87.26	85.74	85.57
29	0.5815	87.09	86.70	86.42
30	0.5784	87.49	86.41	86.22

How to use

from transformers import AutoImageProcessor, AutoModelForImageClassification
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize

model = AutoModelForImageClassification.from_pretrained("matthieulel/dinov2-base-imagenet1k-1-layer-galaxy10-decals-full-v2").eval()
processor = AutoImageProcessor.from_pretrained("matthieulel/dinov2-base-imagenet1k-1-layer-galaxy10-decals-full-v2")
# Pre-processing used in the paper: resize and centre-crop to 256x256 px, then the processor's normalisation
transform = Compose([Resize(256), CenterCrop((256, 256)), ToTensor(), Normalize(processor.image_mean, processor.image_std)])
# logits = model(pixel_values=transform(image).unsqueeze(0)).logits

Integrity check

The model stored in this repository was reloaded and run on the first 96 test images: predictions identical to the saved logits of the selected epoch for 100.0% of them (maximum absolute logit difference 1.91e-06).

Citation

Paper under revision for Astronomy and Computing; citation to be completed upon publication.