Projects sharing features with Racer

UniAD is a unified deep learning framework for autonomous driving that integrates perception, prediction, and planning into a single end-to-end model. It functions as a neural network architecture that maps raw sensor data directly to driving trajectories and motion plans. This project serves as a research implementation of a planning-oriented approach that jointly trains occupancy, mapping, and object tracking modules. It employs a multi-task perception framework to optimize overall driving performance. The system covers a broad capability surface including end-to-end driving pipelines, veh

Apollo is a comprehensive software stack designed for autonomous vehicle development, providing the necessary components for perception, planning, and control. It functions as a high-performance robotics middleware, utilizing a publish-subscribe data bus to facilitate low-latency communication between distributed modules and hardware sensors. The platform integrates data from cameras, lidar, and radar through a sensor fusion framework to generate a real-time environmental model for navigation. The system features a component-based runtime framework that manages task scheduling and resource al

Autoware is an open-source autonomous driving software platform built on the robotics middleware standard. It provides a comprehensive stack for managing perception, planning, and control, enabling the development and deployment of full-stack autonomous driving software on commercial transport hardware. The platform utilizes a component-based modular architecture that organizes driving functions into isolated, interchangeable nodes. This design is supported by a hardware-abstraction layer and plugin-based sensor integration, which allow the software to interface with diverse hardware configur

Openpilot is an open-source driver assistance system that integrates with vehicle control units to provide automated steering, acceleration, and braking. It functions as an automotive robotics middleware, utilizing a specialized runtime environment to process sensor data and execute real-time control commands that manage vehicle dynamics. The platform distinguishes itself through a hardware-agnostic interface that translates standardized driving commands into the proprietary protocols required by a wide range of vehicle makes and models. It employs neural-network-based path planning to predic

This project is a collection of educational resources and reference implementations for neural network development using TensorFlow. It serves as a comprehensive learning course, machine learning curriculum, and practical implementation guide for building deep learning architectures. The codebase provides instructional materials and examples covering a wide range of model types, including convolutional neural networks for image classification, recurrent networks and long short-term memory cells for sequential data, and autoencoders for generative modeling. It also includes implementations for

This project is a collection of PyTorch learning resources and educational guides designed to teach the construction and training of neural networks. It serves as a comprehensive deep learning tutorial covering various model architectures and practical implementation strategies. The resources provide specific guidance on implementing computer vision tasks, such as image classification and synthetic imagery generation, as well as reinforcement learning agents using value networks and experience replay. It also covers sequential data modeling through recurrent networks and generative modeling u

This project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum

GoLearn is a machine learning library for the Go programming language. It provides a supervised learning framework and a toolkit for building, training, and evaluating predictive models through a standardized interface. The project implements a data frame system that loads CSV files into structured grids for matrix operations. It includes a preprocessing library for discretizing continuous variables and a model evaluation toolkit that utilizes confusion matrices and cross-validation to measure precision and recall. The library covers data engineering and management, including the ability to

XUAN is an autonomous vehicle control system that integrates microcontrollers and sensors to manage hardware drivers and AI module communication. It consists of embedded vehicle control firmware, microcontroller hardware designs, and a vehicle chassis CAD model. The project provides the electronic and physical specifications for a bicycle-based vehicle, including PCB schematics and hardware layouts for a controller that manages motor drivers via communication buses and motion sensors. It also includes a set of 3D design files and engineering data for the physical structural frame. The system

SO-ARM100 is an open-source robot arm hardware project providing 3D-printable designs and assembly guides for building affordable robotic arms. It includes calibration software to synchronize motor communication parameters and arm positions via USB, alongside hardware designs for tactile sensing robotic grippers. The project distinguishes itself through the integration of touch-sensing and flexible filaments for adaptive grasping. It also provides a dedicated imitation learning dataset tool, featuring a web interface for labeling and visualizing robotics data to train machine learning models

Brain.js is a JavaScript neural network library for building, training, and running machine learning models in the browser or Node.js. It provides implementations for several network types, including feedforward networks, recurrent neural networks for time series forecasting, and autoencoders for data compression and denoising. The library features WebGL-based GPU acceleration to increase the speed of neural network computations on the graphics processor. It also includes a visualization tool that generates SVG images to represent the topology and layers of a feedforward network. The framewo

This project is a collection of TensorFlow 2.x machine learning tutorials and practical code examples. It serves as a deep learning implementation guide for constructing diverse neural network architectures, including convolutional, recurrent, and generative networks. The repository provides templates and examples for several specialized domains, including computer vision for image classification and object detection, natural language processing for text generation and language understanding, and generative AI for synthesizing data using adversarial networks and autoencoders. It also includes

AISystem is a comprehensive AI full-stack infrastructure project covering the entire pipeline from AI chip architecture to high-level training frameworks. It encompasses the development of AI compiler frameworks, inference engines, and distributed training orchestrators designed to coordinate workloads across a heterogeneous compute stack of CPUs, GPUs, and NPUs. The project focuses on the deep integration of software and hardware, employing software-hardware co-design to align tensor layouts with physical memory structures. It provides specialized capabilities for accelerating Transformer mo

This repository provides a curated collection of self-contained Python code examples that demonstrate the core capabilities of the PyTorch deep learning framework. The examples cover automatic differentiation, dynamic computational graphs, GPU‑accelerated tensor operations, and training of neural network models using gradient‑based optimization. The code samples illustrate PyTorch’s dynamic graph construction, where models can change structure with native control flow, and its automatic gradient computation through reverse‑mode differentiation. Additional examples show how to work with tensor

Baichuan-7B is an open-source 7 billion parameter bilingual Transformer model designed for text generation and few-shot learning across Chinese and English. It is built on a large Transformer architecture trained on a bilingual corpus, enabling it to produce coherent text in both languages from a single model. The model incorporates several optimization techniques that distinguish it from standard large language models. It uses rotary position embeddings that can extrapolate to longer sequences than seen during training, allowing context extension beyond the original 4096-token training lengt

ConvNetJS is a JavaScript deep learning library and neural network training engine designed for client-side machine learning. It functions as a framework for building, training, and running convolutional neural networks directly within a web browser without the need for a backend server. The library specializes in image recognition and pattern analysis using convolutional and pooling layers. It enables the creation of models for classification and regression tasks, as well as the development of reinforcement learning agents that optimize behavior through trial and error in simulated environme

Neuraltalk is an automated image captioning system that generates natural language descriptions for images. It utilizes a deep learning model that integrates a pretrained convolutional neural network for visual feature extraction with a recurrent neural network decoder to produce text sequences. The project provides a full workflow for training and evaluating captioning models, including weight optimization via backpropagation and gradient descent. It includes tools for measuring caption accuracy by comparing generated text against reference descriptions. The system covers data preprocessing

This project is a deep learning educational resource consisting of PyTorch model implementations and code examples. It provides functional Python scripts and notebooks for building, training, and optimizing neural networks using tensor-based computation. The repository includes implementations for designing custom network layers and loss functions, as well as examples of transfer learning workflows that load pretrained model weights to accelerate development. The codebase covers a broad range of deep learning capabilities, including neural network training, custom model component design, and

Deep Java Library is a Java deep learning framework and JVM model inference engine. It provides a high-level API for building and deploying deep learning models within the Java ecosystem, acting as a cross-platform runtime for executing models across CPUs, GPUs, and mobile devices. The library is engine-agnostic, allowing users to switch between different deep learning engines such as PyTorch, TensorFlow, and MXNet while maintaining a single unified API. This enables the deployment of the same model across different backends without changing the application code. The framework supports the f

MLOps-Basics is a collection of implementation guides and blueprints for automating the machine learning lifecycle. It provides practical workflows for managing the transition of models from training to production deployment, focusing on the integration of operational tools into the machine learning pipeline. The project features specific architectural patterns for deploying containerized models using serverless infrastructure and cloud registries. It includes frameworks for tracking large datasets and model artifacts via remote storage, as well as guides for converting models into standardiz

Boto3 is the AWS SDK for Python, providing a programmatic interface for managing and automating AWS cloud infrastructure and services. It serves as a cloud management API client and resource manager for provisioning, configuring, and scaling virtual servers, databases, and storage. The library enables the implementation of infrastructure-as-code through declarative templates and scripts, allowing for the deployment of identical resource stacks across multiple accounts and geographic regions. It also provides a framework for coordinating distributed workflows, serverless functions, and contain

This project is a PyTorch transformer model library and pre-trained model framework. It serves as a deep learning model hub and multimodal inference engine, providing a centralized system for loading, executing, and fine-tuning state-of-the-art model checkpoints. The library focuses on multimodal machine learning, enabling predictions across text, vision, and audio data. It provides specialized capabilities for model framework interoperability, allowing the conversion of weights and definitions between different deep learning libraries. The platform covers the full model lifecycle, including

Nougat is a neural OCR system and LLM document parser designed to convert images of academic PDF documents into structured markdown text and mathematical formulas. It functions as a PDF to markdown converter that uses deep learning to handle layout and formula recognition. The project provides a document training pipeline for generating datasets and training neural networks to recognize specific academic document styles. This includes utilities for training dataset generation, neural model training, and model checkpoint management to ensure reproducible deployment. The system covers a broad

GPT-Neo is an open-source distributed training framework designed for scaling GPT-2 and GPT-3-style language models across multiple devices using mesh-tensorflow for model parallelism. It provides the infrastructure to train transformer-based language models with billions of parameters across distributed computing environments, making large-scale language model research accessible outside of proprietary systems. The framework supports training both autoregressive GPT-style models and masked language models like BERT or RoBERTa, with configurable masking strategies and token handling. It inclu

Caffe is a high-performance deep learning framework designed for training and deploying deep neural networks. It functions as a machine learning engine and a convolutional neural network library, providing a C++ backend to accelerate computations on both GPUs and CPUs. The system includes a specialized toolset for computer vision, enabling tasks such as object detection, semantic segmentation, and large-scale image retrieval. It supports the deployment of pre-trained models for image and scene recognition, as well as the ability to fine-tune neural network weights for specialized tasks. The

FlagAI is a distributed deep learning framework and platform designed for the end-to-end lifecycle of large-scale foundation models. It provides a toolkit for training, fine-tuning, and deploying large language models and multi-modal systems across multi-node computing clusters. The project features hardware-agnostic compute abstractions to ensure consistent execution across different accelerators. It includes a dedicated library for parameter-efficient fine-tuning, allowing large neural networks to be adapted to specific tasks with minimal parameter updates and reduced computational overhead