This is the repository for the BioCLIP model and the TreeOfLife-10M dataset [CVPR'24 Oral].

Meme search engine and recommendation system using CLIP-based neural nets

Keras beit,caformer,CMT,CoAtNet,convnext,davit,dino,efficientdet,edgenext,efficientformer,efficientnet,eva,fasternet,fastervit,fastvit,flexivit,gcvit,ghostnet,gpvit,hornet,hiera,iformer,inceptionnext,lcnet,levit,maxvit,mobilevit,moganet,nat,nfnets,pvt,swin,tinynet,tinyvit,uniformer,volo,vanillanet,yolor,yolov7,yolov8,yolox,gpt2,llama2, alias kecam

Open-source evaluation toolkit of large vision-language models (LVLMs), support GPT-4v, Gemini, QwenVLPlus, 40+ HF models, 20+ benchmarks

Paddle Multimodal Integration and eXploration, supporting mainstream multi-modal tasks, including end-to-end large-scale multi-modal pretrain models and diffusion model toolbox. Equipped with high performance and flexibility.

Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai

[ICML 2024] Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

Implementation of Zero-Shot Contrastive Loss for Text-Guided Diffusion Image Style Transfer.

Official implementation of our IEEE Access paper (2024), ZEN-IQA: Zero-Shot Explainable and No-Reference Image Quality Assessment with Vision Language Model

This is a straightforward image search application using CLIP, developed through a simple Next.js-based website interface.

Image to prompt with BLIP and CLIP

[LDFusion] Official implementation for "Infrared and visible Image Fusion with Language-driven Loss in CLIP Embedding Space"

Transformers 3rd Edition

An AI-powered natural language & reverse Image Search Engine powered by CLIP & qdrant.

Generative AI system for creating glyphs

A Rust library integrated with ONNXRuntime, providing a collection of Computer Vison and Vision-Language models.

This is the official repository for the paper "OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data". ICIAP 2023

Self-paced Curriculum Adapting of CLIP for Visual Grounding.

Framework for learning multi-domain image embeddings suitable for multi-domain image retrieval at instance-level