Rich is a Python library for rich text and beautiful formatting in the terminal.

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

PyTorch Tutorial for Deep Learning Researchers

A generative world for general-purpose robotics & embodied AI learning.

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Python logging made (stupidly) simple

🤗 LeRobot: Making AI for Robotics more accessible with end-to-end learning

End-to-End Object Detection with Transformers

[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

Common used path planning algorithms with animations.

A resource for learning about Machine learning & Deep Learning

Object detection, 3D detection, and pose estimation using center point detection:

Minimal PyTorch implementation of YOLOv3

[ICLR 2024] Efficient Streaming Language Models with Attention Sinks

Unified framework for robot learning built on NVIDIA Isaac Sim

[NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"

OpenVLA: An open-source vision-language-action model for robotic manipulation.

Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

Official DeiT repository

A python parametric CAD scripting framework based on OCCT

Deformable DETR: Deformable Transformers for End-to-End Object Detection.

A PyTorch implementation of the YOLO v3 object detection algorithm

Code release for "Masked-attention Mask Transformer for Universal Image Segmentation"

Robotics Toolbox for Python

Collection of reinforcement learning algorithms

[ECCV 2024] Official implementation of the paper "Semantic-SAM: Segment and Recognize Anything at Any Granularity"