birder-project/rope_vit_reg8_b14_nps_avg_capi-dino-bio
https://huggingface.co/birder-project/rope_vit_reg8_b14_nps_avg_capi-dino-bioA RoPE ViT Reg8 B/14 image encoder with average pooling, pretrained using CAPI-DINO on natural biological images. This model has not been fine-tuned for a specific classification task and is intended to be used as a general-purpose feature extractor or a backbone for downstream tasks like object…
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- HuggingFace — birder-project/rope_vit_reg8_b14_nps_avg_capi-dino-bio
Related resources
vitreg4so150mp14ls_dino-v2-bio is a Bio-DINO image encoder for natural photographs of living organisms. It uses a SoViT-150M/14 Vision Transformer with 4 register tokens and 133.6M backbone parameters, trained with a DINOv2-style self-supervised objective on approximately 31 million curated images…
vitreg1s14lsdino-v2-dist-bio is a compact Bio-DINO image encoder distilled from the larger Bio-DINO SoViT-150M/14 model. It keeps the same natural-photography biodiversity scope as the teacher model, but uses a much smaller ViT-S/14-style student with 21.7M backbone parameters and 384-dimensional…
birder-project/dino_v2_vit_reg4_so150m_p14_ls_bio
by birder-projectThis repository contains the full Bio-DINO DINOv2 training weights for a SoViT-150M/14 Vision Transformer trained on natural photographs of living organisms. It is the companion release to the Birder backbone checkpoints at .
> [!IMPORTANT] > 🎉 Check out the latest version of Phikon here: Phikon-v2 > > Phikon is a self-supervised learning model for histopathology trained with iBOT.
prov-gigapath/prov-gigapath-flash
by prov-gigapathFremyCompany/BioLORD-2023
by FremyCompany# FremyCompany/BioLORD-2023 This model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts.