metadata
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) — same strategy
ViT-Base (DeiT-Base/16, 224) pre-trained on FineFractalDB with FDSML (Formula-Driven Supervised Metric Learning) using the Parameter-Aware Triplet Loss and the same triplet selection strategy. No natural images are used during pre-training. The main ViT-Base results in the paper use the 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 | same (same_class): selects all pairs from the same class |
| Projection dim | 256 |
| Training images seen | 536,870k (536.87 epochs) |
| Checkpoint | checkpoint-536870kimg-536.87ep_fdsml_deitbase_same.pt |
| Name in the official code | deit_base_patch16_224_mocov3_fdsml_same_pretrained |
Usage
The checkpoint is a plain PyTorch file and is loaded with the official code.
import torch
from huggingface_hub import hf_hub_download
path = hf_hub_download("fdsml/fdsml-finefractaldb-vitbase-same", "checkpoint-536870kimg-536.87ep_fdsml_deitbase_same.pt")
ckpt = torch.load(path, map_location="cpu")
To fine-tune on a downstream task, pass the checkpoint to finetune.py:
torchrun --standalone --nproc_per_node=4 finetune.py \
--net=deit_base_patch16_224_mocov3 \
--ckpt=/path/to/checkpoint-536870kimg-536.87ep_fdsml_deitbase_same.pt \
--dset=/path/to/imagenet
See the README for the full list of options and the exact configurations used in the paper.
Citation
@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. Non-commercial use only.