VyomAI: state-of-the-art NLP LLM Vision MultiModel transformers implementation into Pytorch
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Updated
Jun 2, 2024 - Python
VyomAI: state-of-the-art NLP LLM Vision MultiModel transformers implementation into Pytorch
Advanced Invoice Reader Powered by Google Gemini Pro Multimodal AI and Multi Lingual
The Multi-Modal Search Engine is a cutting-edge project that integrates OpenAI's CLIP model into a user-friendly web interface. With intuitive search functionality and seamless integration of text queries to retrieve relevant images, this project demonstrates the potential of multi-modal search systems.
🧘🏻♂️KarmaVLM (相生):A family of high efficiency and powerful visual language model.
Awesome_Multimodel is a curated GitHub repository that provides a comprehensive collection of resources for Multimodal Large Language Models (MLLM). It covers datasets, tuning techniques, in-context learning, visual reasoning, foundational models, and more. Stay updated with the latest advancement.
Arogya-Shree-Medi-Bot
This project is a multi-modal model that works with multiple models combined and accepts audio, images, and text as inputs, generating corresponding audio, images, and text outputs.
ArangoGraph is the easiest way to run ArangoDB. Available on AWS, Google Cloud & Azure.
📄 SemEval 2024 Task 8: Artificial Intelligence Text Detection System using Natural Language Processing and Neural Network techniques.
yolov5, yolov8, segmenations, face, pose, keypoints on deepstream
Simplify time-consuming coding for the data scientist. Create beautiful charts, pandas transformers, and find the best model with the best parameters for your data.
large model Zoo collect various of large-scale model, include CV and NLP, multiModel Etc.
RMDL: Random Multimodel Deep Learning for Classification
🐝 Research on the Control and Prediction of the North American Bumblebee Using Multimodal Algorithms.
OpenVINO+NCS2/NCS+MutiModel(FaceDetection, EmotionRecognition)+MultiStick+MultiProcess+MultiThread+USB Camera/PiCamera. RaspberryPi 3 compatible. Async.
Accepted by TMM 2022
Implementation of HUSE: Hierarchical Universal Semantic Embeddings https://arxiv.org/pdf/1911.05978.pdf
This is our solution for KDD Cup 2020. We implemented a very neat and simple neural ranking model based on siamese BERT which ranked first among the solo teams and ranked 12th among all teams on the final leaderboard.
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