DermLIP: Dermatology Language Image Pretraining Model Description DermLIP is a vision language model for dermatology, trained on the Derm1M dataset—the largest dermatological image text corpus to date. Model Details Model Type: Pretrained Vision Language Model (CLIP style) Architecture: Vision encoder : ViT B16 Text encoder : GPT2 Resolution: 224×224 pixels Paper: https://arxiv.org/abs/2503.14911 Repository: https://github.com/SiyuanYan1/Derm1M license: cc by nc nd 4.0 Training Details Training data: 403,563 skin image text pairs from Derm1M datasets. Images include both dermoscopic and clinical images. Training objective: image text contrastive loss Hardware: 1 x Nvidia H200 （～40GB memory usage） Hours used: ~5 hours Intended Uses Primary Use Cases Zero shot classification Few shot learning Cross modal retrieval Concept annotation/explanation How to Use Installation First, clone the Derm1M repository: python import open clip from PIL import Image import torch Load model with huggingface checkpoint model, , preprocess = open clip.create model and transforms( 'hf hub:redlessone/DermLIP ViT B 16' ) model.eval() Initialize tokenizer tokenizer = open clip.get tokenizer('hf hub:redlesson…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy