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license: cc-by-nc-sa-4.0
library_name: pytorch
pipeline_tag: image-feature-extraction
datasets:
- fdsml/FineFractalDB
tags:
- fdsml
- formula-driven-supervised-learning
- metric-learning
- fractal
- synthetic-data
- pre-training
- vit
- arxiv:2610.39423
---
# FDSML ViT-Base (DeiT-Base/16, 224) — `diff` strategy
ViT-Base (DeiT-Base/16, 224) pre-trained on [FineFractalDB](https://huggingface.co/datasets/fdsml/FineFractalDB) with **FDSML** (Formula-Driven Supervised Metric Learning) using the Parameter-Aware Triplet Loss and the **`diff`** triplet selection strategy. No natural images are used during pre-training. The main ViT-Base results in the paper use the [`each`](https://huggingface.co/fdsml/fdsml-finefractaldb-vitbase-each) strategy.
- **Paper:** https://arxiv.org/abs/2610.39423
- **Project page:** https://shineiarakawa.github.io/fdsml/
- **Code:** https://github.com/ShineiArakawa/fdsml
- **Dataset:** https://huggingface.co/datasets/fdsml/FineFractalDB
## Model details
| Item | Value |
|---|---|
| Architecture | ViT-Base (DeiT-Base/16, 224) (86.6M parameters) |
| Pre-training data | FineFractalDB (1,000 IFS classes × 1,000 images, synthetic) |
| Objective | Parameter-Aware Triplet Loss |
| Triplet strategy | `diff` (diff_class): selects all pairs from different classes |
| Projection dim | 256 |
| Training images seen | 524,288k (524.29 epochs) |
| Checkpoint | `checkpoint-524288kimg-524.29ep_fdsml_deitbase_diff.pt` |
| Name in the official code | `deit_base_patch16_224_mocov3_fdsml_diff_pretrained` |
## Usage
The checkpoint is a plain PyTorch file and is loaded with the [official code](https://github.com/ShineiArakawa/fdsml).
```python
import torch
from huggingface_hub import hf_hub_download
path = hf_hub_download("fdsml/fdsml-finefractaldb-vitbase-diff", "checkpoint-524288kimg-524.29ep_fdsml_deitbase_diff.pt")
ckpt = torch.load(path, map_location="cpu")
```
To fine-tune on a downstream task, pass the checkpoint to `finetune.py`:
```bash
torchrun --standalone --nproc_per_node=4 finetune.py \
--net=deit_base_patch16_224_mocov3 \
--ckpt=/path/to/checkpoint-524288kimg-524.29ep_fdsml_deitbase_diff.pt \
--dset=/path/to/imagenet
```
See the [README](https://github.com/ShineiArakawa/fdsml#fine-tuning) for the full list of options and the exact configurations used in the paper.
## Citation
```bibtex
@inproceedings{Otake2027fdsml,
title = {FDSML: Formula-Driven Supervised Metric Learning with Parameter-Aware Triplet Loss},
author = {Hina Otake and Shinei Arakawa and Keitaro Tanaka and Yoshihiro Fukuhara and Hirokatsu Kataoka and Shigeo Morishima},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
year = {2027},
}
```
## License
Released under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). Non-commercial use only.
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