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Automate browser-based workflows with LLMs and Computer Vision
Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models
Neural Networks: Zero to Hero
Free MLOps course from DataTalks.Club
Simple and comprehensive tutorials in TensorFlow
Best Practices, code samples, and documentation for Computer Vision.
Convert Machine Learning Code Between Frameworks
This repository explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both…
🐶 A tool to package, serve, and deploy any ML model on any platform. Archived to be resurrected one day🤞
🎓 Sharing machine learning course / lecture notes.
Materials for The Alan Turing Institute's Research Software Engineering course
A topic-centric list of HQ open datasets.
A powerful data & AI notebook templates catalog: prompts, plugins, models, workflow automation, analytics, code snippets - following the IMO framework to be searchable and reusable in any context.
The fastai book, published as Jupyter Notebooks
Python notebooks and slides for CE9010: Introduction to Data Science, Semester 2 2017/18
Download hundreds of images from Google. Do image post processing later.
This repo is only for students at DYPIEMR, Pune. In Artificial Intelligence and Data Science branch.
Example notebooks for working with SageMaker Studio Lab. Sign up for an account at the link below!
A unified 3D Transformer Pipeline for visual synthesis
Food Vision project for mobile devices using TensorFlowJS