clip-vit-base-patch16

Model · Zero shot image classification

clip-vit-base-patch32

OpenAI

Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here. The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within. January 2021 The model uses a ViT-B/32 Transformer architecture as an image encoder and uses a masked self-attention…

Model · Zero shot image classification

clip-vit-large-patch14-336

OpenAI

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - trainingprecision: float32 - Transformers 4.21.3 - TensorFlow 2.8.2 - Tokenizers 0.12.1

Model · Zero shot image classification

CLIP-convnext_base_w-laion2B-s13B-b82K-augreg

LAION eV

A series of CLIP ConvNeXt-Base (w/ wide embed dim) models trained on subsets LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/openclip). Explore an alternative to ViT and ResNet (w/ AttentionPooling) CLIP models that scales well with model size and image resolution First known ConvNeXt CLIP models trained at scale in the range of CLIP ViT-B/16 and RN50x4 models First released model weights exploring increase of augmentation + regularization for image tower via adding (greater scale range of RRC, random erasing, stochastic depth) The models utilize the timm ConvNeXt-Base model (convnextbase) as the image tower, and the same text tower as the RN50x4…

Model · Zero shot image classification

clip-vit-base-patch16

OpenAI

Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here. The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within. January 2021 The base model uses a ViT-B/16 Transformer architecture as an image encoder and uses a masked…

DermLIP is a vision-language model for dermatology, trained on the Derm1M dataset—the largest dermatological image-text corpus to date. python import openclip from PIL import Image import torch model,, preprocess = openclip.createmodelandtransforms( 'hf-hub:redlessone/DermLIPViT-B-16' model.eval() tokenizer = openclip.gettokenizer('hf-hub:redlessone/DermLIPViT-B-16') image = preprocess(Image.open("yourskinimage.png")).unsqueeze(0) PADCLASSNAMES = [ "nevus", "basal cell carcinoma", "actinic keratosis", "seborrheic keratosis", "squamous cell carcinoma", "melanoma" template = lambda c: f'This is a skin image of {c}' text = tokenizer([template(c) for c in PADCLASSNAMES]) with torch.nograd()…

DermFM-Zero: A Vision-Language Foundation Model for Zero-shot Clinical Collaboration and Automated Concept Discovery in Dermatology DermFM-Zero pairs a PanDerm ViT-L/16 vision encoder with a knowledge-enhanced PubMedBERT text encoder, aligned on dermatology image–text pairs with multi-aspect knowledge contrastive learning. It classifies dermoscopic and clinical images from text prompts without task-specific training, retrieves images from text and text from images, serves as a backbone for multimodal fine-tuning and VQA, and exposes interpretable concepts through sparse autoencoders. In the paper, it was evaluated on diverse benchmarks and in three multinational reader studies with 761…