# CLIP visual encoding

ResNet101 image encoder based on CLIP framework, extracting image features through Global Average Pooling (GAP)

CLIP model image encoder based on ResNet50 architecture with 64x width expansion, using Global Average Pooling (GAP) strategy

A ResNet50x16 variant model based on the CLIP framework, focused on image feature extraction

ResNet50x4 variant model based on the CLIP framework, designed for image feature extraction

A vision transformer model based on the CLIP architecture, focused on image feature extraction, released by Apple.

A ViT-Huge image encoder based on the CLIP architecture, released by Apple as part of the DFN5B-CLIP model, suitable for visual feature extraction tasks.

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

ViT-Huge visual encoder based on the CLIP framework, trained on the laion2B dataset, supports image feature extraction

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 on the laion2B dataset

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 Base model based on the CLIP framework, trained on the LAION-Aesthetic dataset, suitable for image feature extraction tasks.

A vision-language model fine-tuned from Microsoft's git-base model, specifically designed to generate descriptive text captions for images from the anime 'One Piece'