Initial commit

42b8259 12 days ago

1.71 kB

tags:
  - image-feature-extraction
  - timm
  - vision-transformer
  - moco-v3
  - self-supervised-learning
  - precision-at-scale
library_name: timm
license: cc-by-nc-4.0
datasets:
  - jesusmolrdv/pas-i

mocov3-vitb16-pas-insects

MoCo-v3 ViT-B/16 backbone, self-supervised pretrained from scratch on the PaS-I domain-specific dataset (Insects, ~1.9M images) generated by the Precision at Scale pipeline.

Training data

Pretrained on jesusmolrdv/pas-i, 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}
}

arXiv preprint

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-insects", pretrained=True)
model.eval()

model outputs 768-d backbone features (no classification head attached).