mocov3-vitb16-pas-food
MoCo-v3 ViT-B/16 backbone, self-supervised pretrained from scratch on the
PaS-F domain-specific dataset (Food, 1.2M images) generated by
the Precision at Scale pipeline.
Training data
Pretrained on jesusmolrdv/pas-f,
the Precision at Scale domain-specific dataset for this checkpoint.
Paper
- Paper (Pattern Recognition, 2026): https://doi.org/10.1016/j.patcog.2025.112236
- Official code: https://github.com/jesusmolrdv/Precision-at-Scale
@article{rodriguezdevera2026precision,
title = {Precision at scale: Domain-specific datasets on-demand},
author = {Rodr{\'i}guez-de-Vera, Jes{\'u}s M. and Estepa, Imanol G. and Saras{\'u}a, Ignacio and Nagarajan, Bhalaji and Radeva, Petia},
journal = {Pattern Recognition},
volume = {171},
pages = {112236},
year = {2026},
publisher = {Elsevier},
doi = {10.1016/j.patcog.2025.112236}
}
https://arxiv.org/abs/2407.03463
Usage
This backbone is stored in plain timm
format (vit_base_patch16_224, num_classes=0), so it loads directly
through the timm Hugging Face Hub integration:
import timm
model = timm.create_model("hf_hub:jesusmolrdv/mocov3-vitb16-pas-food", pretrained=True)
model.eval()
model outputs 768-d backbone features (no classification head attached).