Xieji-Li/MAGEN-O-MAKE

BiomedCLIP is a biomedical vision-language foundation model that is pretrained on PMC-15M, a dataset of 15 million figure-caption pairs extracted from biomedical research articles in PubMed Central, using contrastive learning.

BioCAP is a foundation model for biology organismal images. It is trained on TreeOfLife-10M with synthetic captions (TreeOfLife-10M-Captions) as supervision on the basis of a CLIP model (ViT-B/16) pre-trained by OpenAI. BioCAP achieves state-of-the-art performance on text-image retrieval tasks.

BioCLIP is a foundation model for the tree of life, built using CLIP architecture as a vision model for general organismal biology. It is trained on TreeOfLife-10M, our specially-created dataset covering over 450K taxa--the most biologically diverse ML-ready dataset available to date.

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DermLIP is a vision-language model for dermatology, trained on the Derm1M dataset—the largest dermatological image-text corpus to date. This model variant (PanDerm-base-w-PubMed-256) utilizes domain-specific pretraining to deliver superior performance compared to other DermLIP variants..