Making large AI models cheaper, faster and more accessible
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Updated
Apr 28, 2023 - Python
Making large AI models cheaper, faster and more accessible
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
ChatGPT with video understanding! And many more supported LMs such as miniGPT4, StableLM, and MOSS.
EVA Series: Visual Representation Fantasies from BAAI
TorchXRayVision: A library of chest X-ray datasets and models.
Interactive data structures for evaluating foundation models.
InternVideo: General Video Foundation Models via Generative and Discriminative Learning (https://arxiv.org/abs/2212.03191)
Segment-anything related awesome extensions/projects/repos.
Official Code for "Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents"
PyTorch implementation of BEVT (CVPR 2022) https://arxiv.org/abs/2112.01529
Largest pre-trained medical image segmentation model (1.4B parameters) based on the largest public dataset (>100k annotations), up until April 2023.
This repository holds code and other relevant files for the NeurIPS 2022 tutorial: Foundational Robustness of Foundation Models.
Sample for Intelligent app dev workshop to demonstrate the potential of integrating SoTA foundation models in user experiences and backend workflows. Orchestrated by Semantic Kernel and built on Azure primitives.
Official implementation for CVPR'23 paper "BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning"
A curated list of foundation models for vision and language tasks
Self-supervised learning for wearables using the UK-Biobank (>700,000 person-days)
Immersive workshop showcasing the remarkable potential of integrating SoTA foundation models to enhance user experiences and streamline backend workflows. Leverages Semantic Kernel and Azure primitives to offer an engaging and comprehensive introduction to AI-infused app development and deployment
CLI for managing and generating Foundation Model prompts
FEMR (Framework for Electronic Medical Records) provides tooling for large-scale, self-supervised learning using electronic health records
Object Detection with Vision-Language Pre-training
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