# Zero-shot image classification

A lightweight vision-language model based on the SigLIP architecture, extracting knowledge from the larger SigLIP-400m model through distillation techniques, suitable for zero-shot image classification tasks.

This is a pre-trained model for researching backdoor sample detection in contrastive language-image pre-training, containing a specific backdoor trigger BLTO.

SigLIP 2 vision-language model trained on WebLI dataset, supporting zero-shot image classification

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

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

SigLIP 2 vision-language model trained on WebLI dataset, supporting zero-shot image classification

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

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

SigLIP 2 vision-language model trained on WebLI dataset, supporting zero-shot image classification

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

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

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

SigLIP 2 vision-language model trained on the WebLI dataset, supporting zero-shot image classification tasks

SigLIP 2 is a vision-language 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 multilingual vision-language encoder based on SigLIP, enhancing 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.

This is a vision-language model based on the OpenCLIP framework, trained on the LAION-2B English subset, excelling in zero-shot image classification and cross-modal retrieval tasks.

A vision-language model trained on the LAION-2B English dataset based on the OpenCLIP framework, supporting zero-shot image classification and cross-modal retrieval

The EVA CLIP model is a vision-language model based on OpenCLIP and the timm framework, supporting zero-shot image classification tasks.

The EVA CLIP model is a vision-language model based on OpenCLIP and timm model weights, supporting tasks such as zero-shot image classification.

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

EVA-CLIP is a vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks.

The EVA CLIP model is a vision-language model built on the OpenCLIP and timm frameworks, supporting tasks like zero-shot image classification.

ViT-Huge image encoder based on CLIP architecture, trained on DFN5B dataset, supports quick GELU activation

The visual encoder component of DFN5B-CLIP, based on ViT-Huge architecture, trained with 378x378 resolution images for CLIP model

Vision Transformer model based on CLIP architecture, featuring DFN2B-CLIP image encoder weights released by Apple

Vision Transformer model based on CLIP architecture, specialized in image feature extraction with support for 256x256 resolution input

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

A vision Transformer model based on the CLIP architecture, specifically designed for image feature extraction, using ViT-B/16 structure and trained on the DataComp XL dataset

ConvNeXt-XXLarge image encoder based on the CLIP framework, trained by LAION, suitable for multimodal tasks

CLIP model based on ViT-Huge architecture, supporting zero-shot image classification tasks

A Long-CLIP model fine-tuned with Sparse Autoencoder (SAE), supporting long-text input and optimized for text-image alignment

LLM2CLIP is an innovative approach that enhances CLIP's visual representation capabilities through large language models (LLMs), significantly improving cross-modal task performance

A dual-framework compatible vision model trained on MetaCLIP-2.5B dataset, supporting both OpenCLIP and timm frameworks

A dual-purpose vision-language model trained on the MetaCLIP-2.5B dataset, supporting zero-shot image classification tasks

A dual-framework compatible vision model trained on MetaCLIP-2.5B dataset, supporting zero-shot image classification tasks

Vision Transformer model trained on MetaCLIP-400M dataset, supporting zero-shot image classification tasks

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

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

A vision Transformer model trained on the MetaCLIP-2.5B dataset, compatible with both open_clip and timm frameworks