dinov2-large-galaxy10-decals-full-v2

facebook/dinov2-large 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 10 of 30)	85.96	85.56
Mean +/- s.d. over three seeds (42, 1, 2)	85.19 +/- 0.74	84.85 +/- 0.63

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 10 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	0.7792	73.96	74.69	71.42
2	0.5722	80.61	81.51	81.24
3	0.6568	77.62	76.89	76.40
4	0.5428	82.30	81.62	81.56
5	0.5354	83.26	82.36	82.02
6	0.4629	84.39	84.27	84.06
7	0.4927	83.82	84.22	83.92
8	0.4688	85.01	84.27	84.14
9	0.4560	85.63	85.12	84.77
10 (selected)	0.4432	86.30	85.96	85.56
11	0.4770	85.57	85.57	85.54
12	0.4688	86.47	85.40	85.06
13	0.4538	86.36	85.17	85.02
14	0.4822	85.57	84.16	83.79
15	0.5054	86.70	85.34	85.03
16	0.5149	85.96	85.17	84.72
17	0.5906	86.41	86.13	85.67
18	0.6182	85.79	86.81	86.71
19	0.6309	85.01	85.40	85.36
20	0.5976	86.19	87.09	87.11
21	0.6297	86.92	85.34	84.82
22	0.6395	86.58	86.36	86.19
23	0.6411	86.98	86.13	86.16
24	0.6789	86.13	86.08	86.06
25	0.6387	85.96	86.02	85.92
26	0.6884	87.09	87.09	86.91
27	0.6685	86.92	86.30	86.09
28	0.6483	87.43	86.98	86.76
29	0.6594	87.15	86.41	86.20
30	0.6456	87.71	87.15	86.92

How to use

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

model = AutoModelForImageClassification.from_pretrained("matthieulel/dinov2-large-galaxy10-decals-full-v2").eval()
processor = AutoImageProcessor.from_pretrained("matthieulel/dinov2-large-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.

matthieulel/dinov2-large-galaxy10-decals-full-v2