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.

Google Research

100-Days-Of-ML-Code中文版

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)

High-Resolution Image Synthesis with Latent Diffusion Models

LAVIS - A One-stop Library for Language-Vision Intelligence

Code release for NeRF (Neural Radiance Fields)

My blogs and code for machine learning. http://cnblogs.com/pinard

Reference models and tools for Cloud TPUs.

Book_1_《编程不难》 | 鸢尾花书：从加减乘除到机器学习；请多多批评指正！

This is the official code for MobileSAM project that makes SAM lightweight for mobile applications and beyond!

Python-based tools for document analysis and OCR

Efficient few-shot learning with Sentence Transformers

Welcome to the Physics-based Deep Learning Book (v0.2)

Official implementation of "Segment Any Anomaly without Training via Hybrid Prompt Regularization (SAA+)".

This library augments road images to introduce various real world scenarios that pose challenges for training neural networks of Autonomous vehicles. Automold is created to train CNNs in specific w…

Large Language Models Are Reasoning Teachers (ACL 2023)

A repository contains more than 12 common statistical machine learning algorithm implementations. 常见机器学习算法原理与实现

Official code for paper "On the Connection between Local Attention and Dynamic Depth-wise Convolution" ICLR 2022 Spotlight

Holds code for our CVPR'23 tutorial: All Things ViTs: Understanding and Interpreting Attention in Vision.

Labeling is boring. Use this tool to speed up your next object detection project!

machine learning algorithms and implementations

Export utility for unconstrained channel pruned models

training imagenet2012 with pytorch

AQuA: A Benchmarking Tool for Label Quality Assessment