# Zero-shot Image Classification

FG-CLIP is a fine-grained visual and text alignment model that achieves global and region-level image-text alignment through two-stage training.

OpenVision is a fully open, cost-effective family of advanced visual encoders focused on multimodal learning.

OpenVision is a fully open, cost-effective family of advanced vision encoders focused on multimodal learning.

SigLIP 2 vision-language model trained on WebLI dataset, suitable for zero-shot image classification tasks.

A SigLIP 2 vision-language model trained on the WebLI dataset, suitable for zero-shot image classification tasks.

A SigLIP 2 vision-language model trained on the WebLI dataset, suitable for zero-shot image classification tasks.

A SigLIP 2 vision-language model trained on the WebLI dataset, suitable for zero-shot image classification tasks.

SigLIP 2 is an improved model based on the SigLIP pre-training objective, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is a multilingual vision-language encoder that integrates SigLIP's pretraining objectives and introduces new training schemes, enhancing semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is a vision-language model based on SigLIP, enhanced with improved semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved model based on the SigLIP pre-training objective, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved model based on SigLIP, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved model based on the SigLIP pretraining objective, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an advanced vision-language model that integrates multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved multilingual vision-language encoder based on SigLIP, enhancing semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved vision-language model based on SigLIP, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is a vision-language model that integrates multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is a vision-language model based on SigLIP, enhancing semantic understanding, localization, and dense feature extraction through a unified training approach.

SigLIP 2 is a multilingual vision-language encoder with improved semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved multilingual vision-language encoder based on SigLIP, enhancing semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved version of SigLIP, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

mmE5 is a multimodal multilingual embedding model trained on Llama-3.2-11B-Vision, improving embedding performance through high-quality synthetic data and achieving state-of-the-art results on the MMEB benchmark.

A zero-shot image classification model based on the open_clip library, specializing in medical field image analysis

GenMedClip is a zero-shot image classification model based on the open_clip library, specializing in medical image analysis.

A vision-language model fine-tuned based on CLIP ViT-L/14, optimized for abstract image-text retrieval tasks

EVA02 CLIP is a large-scale vision-language model based on the CLIP architecture, supporting tasks such as zero-shot image classification.

A vision Transformer model based on SigLIP, focusing on image feature extraction with original attention pooling mechanism.

Vision Transformer model based on SigLIP, utilizing global average pooling for image features

A vision Transformer model based on SigLIP, containing only an image encoder, utilizing the original attention pooling mechanism.

Vision Transformer model based on SigLIP architecture, containing only the image encoder part, using original attention pooling mechanism

Vision Transformer model based on SigLIP, containing only the image encoder part, utilizing original attention pooling mechanism

A vision Transformer model based on the CLIP architecture, specifically designed for image feature extraction, released by the LAION organization.

Vision Transformer model based on CLIP architecture, designed for image feature extraction, trained on the laion2B dataset

ConvNeXt Base image encoder based on the CLIP framework, trained on the LAION-2B dataset, supports image feature extraction

CLIP image encoder based on ConvNeXt architecture, trained by LAION, suitable for multimodal vision-language tasks

A CLIP model fine-tuned with sparse autoencoder (SAE), excelling in zero-shot image classification tasks, particularly adept at recognizing adversarial typographic attacks

A large Vision Transformer model trained on the LAION-400M dataset, supporting zero-shot image classification tasks

A vision-language dual-purpose model trained on the LAION-400M dataset, supporting zero-shot image classification tasks

A dual-framework compatible vision model trained on the MetaCLIP-400M dataset, supporting both OpenCLIP and timm frameworks

Vision Transformer model trained on the LAION-400M dataset, compatible with both open_clip and timm frameworks