Projects sharing features with PaddleX

tensorrtx is a computer vision inference engine and model implementation library designed for graphics processor acceleration. It provides a framework for optimizing deep learning models through a GPU inference optimizer, a deep learning model converter for transforming weights from frameworks like TensorFlow and PyTorch, and a custom plugin library to implement operations not natively supported by the TensorRT API. The project distinguishes itself through a comprehensive collection of pre-defined network implementations, ranging from various YOLO versions and DETR transformers for object det

PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti

SAHI is a sliced inference framework and computer vision pipeline designed to detect small objects in high-resolution images. It provides a system for dividing large images into overlapping patches to prevent the detail loss that typically occurs during standard model downscaling, alongside an image tiling utility and a COCO dataset toolkit. The project distinguishes itself by offering a model-agnostic prediction wrapper that standardizes different machine learning frameworks into a unified interface. This allows it to implement sliced inference and object detection across various model backe

ImageAI is a Python computer vision library providing a suite of tools for image classification, object detection, and video analytics. It functions as an integrated framework for locating and labeling objects in static images and video streams, utilizing deep learning models for identification and categorization. The project includes a model training toolkit that allows for the creation of custom classifiers and detectors through scratch training or transfer learning. It features a GPU-accelerated inference engine to increase processing speed for vision tasks and includes specialized utiliti

PandaOCR is a desktop application for extracting text from images and screen captures using optical character recognition. It functions as a mathematical formula digitizer, a table data extractor, a multilingual translation utility, and a text-to-speech interface. The project distinguishes itself through specialized recognition routing that distributes data across different providers based on whether the content is standard text, tables, or formulas. It provides real-time software interface localization by rendering translated text layers directly over active application windows using coordin

FastDeploy is a high-performance deployment framework for large language models, vision models, and multimodal models. It provides the infrastructure to launch model services that process combined image, video, and text inputs, exposing these capabilities through a standardized, OpenAI-compatible API for chat and text completions. The project distinguishes itself through advanced inference pipeline engineering and GPU optimization. It employs speculative decoding, tensor parallelism, and a disaggregated execution model that separates prefill and decode phases across different hardware resourc

This project is an automated image translation system and pipeline specifically optimized for manga and comics. It provides a sequence of text detection, machine translation, and typesetting, and is available as an image translation API, a command-line tool for batch processing, and an LLM-powered translator. The system utilizes OCR to detect text regions and an inpainter to remove original content by synthesizing background pixels. Translated text is then overlaid using an automated typesetter that manages font sizes, colors, and reading directions based on the original coordinates. The wor

TensorFlow Serving is a high-performance machine learning inference server designed to deploy TensorFlow models to production environments. It functions as a complete serving system that executes predictions on input data through a graph executor, providing network endpoints that eliminate the need for a separate runtime environment for client applications. The system is distinguished by its model version manager, which organizes and selects specific model versions within a directory hierarchy. It uses a filesystem watcher to detect new model versions and trigger automatic updates without int

This is a comprehensive deep learning course delivered entirely through Jupyter Notebooks, designed to teach neural network construction using TensorFlow 2.x. The curriculum follows a sequential-model-first pedagogy, introducing the Sequential API before moving to functional and subclassing approaches, and covers the full spectrum of model building from regression and classification through convolutional neural networks, natural language processing, and time series forecasting. The course is structured around a checkpoint-based training workflow that saves the best model weights during traini

Donut is an OCR-free document transformer and end-to-end document parser. It functions as a neural network that converts unstructured document images directly into structured data or text without the use of an external optical character recognition engine. The project includes a synthetic document generator to create artificial images and ground-truth labels for training. It employs a transformer model to perform visual question answering and document image classification based on visual layout and text. The system covers several document understanding capabilities, including structured info

bert4keras is a lightweight reimplementation of the BERT transformer architecture for the Keras deep learning framework. It serves as a natural language processing toolkit and transformer model library used for text classification, sequence labeling, and semantic embedding extraction. The framework includes a sequence-to-sequence model system for question answering and text generation, as well as a model inference server to deploy trained transformers as web APIs for real-time predictions. Capabilities cover a broad range of natural language understanding tasks, including reading comprehensi

This project is a collection of educational resources and implementation frameworks providing deep learning model recipes, code samples, and step-by-step guides for computer vision tasks. It organizes complex workflows into modular recipes and implementation guides to facilitate the building of image and video analysis models. The framework focuses on specialized vision capabilities, including an image similarity framework for fast retrieval and re-ranking, human pose estimation, and video action recognition. It also provides specific tools for crowd density estimation and document image clea

VLM-R1 is a reasoning vision-language model and embodied AI framework designed to map visual inputs and language instructions into physical navigation waypoints and robotic actions. It functions as a multimodal policy optimizer and an open vocabulary detector capable of locating objects based on arbitrary natural language descriptions. The system distinguishes itself through the use of chain-of-thought reasoning and reinforcement learning to solve complex visual and spatial tasks. It utilizes a video semantic memory system, which employs a visual cache to maintain a history of live video for

This project is a PyTorch-based computer vision library and deep learning image processing framework. It provides a collection of neural network architectures designed for visual analysis tasks, specifically focusing on image classification, object detection, and semantic segmentation. The toolset implements diverse methodologies for visual recognition, including anchor-free object detection, regional proposal networks, and heatmap-based keypoint estimation. It utilizes both convolutional neural networks for spatial feature extraction and transformer-based self-attention mechanisms to compute

This project is a comprehensive educational resource and tutorial handbook for building, training, and deploying machine learning models using TensorFlow 2. It serves as a structured learning guide covering core deep learning concepts, including neural network architectures, automatic differentiation, and tensor operations. The handbook provides technical guidance on optimizing execution efficiency through GPU memory management, distributed training, and model quantization. It also includes detailed manuals for constructing high-performance data pipelines and exporting models for production s

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

Gluon-CV is an MXNet computer vision library that provides a comprehensive collection of pre-implemented vision architectures and training pipelines. It serves as a deep learning research toolkit and a model zoo containing state-of-the-art pre-trained weights for image and video analysis. The project includes a specialized human pose estimation library and a model compression toolkit. These tools allow for the pruning and quantization of deep learning models to increase inference speed and facilitate deployment on constrained edge hardware. The library covers a broad range of vision capabili

chineseocr_lite is a lightweight Chinese optical character recognition engine designed to detect text regions, analyze orientation, and convert Chinese characters from images into digital text. It supports both horizontal and vertical reading layouts and can be deployed as a web service for image uploads and result visualization. The system utilizes a multi-backend inference framework that supports ncnn, mnn, and tnn, allowing it to run across diverse hardware and platforms. It is specifically engineered for lightweight deployment on mobile and desktop environments through the use of small mo

Seldon Core is a Kubernetes-based machine learning model server and MLOps inference framework. It functions as a multi-model serving engine and pipeline orchestrator, packaging models as scalable microservices that are exposed via standardized REST and gRPC APIs. The project distinguishes itself through graph-based inference pipelines that chain models and data transformers into sequential workflows. It optimizes hardware utilization via multi-model shared serving and dynamic memory overcommit strategies, while supporting production experimentation through weighted traffic routing, A/B testin

This project is a structured educational program and machine learning engineering course. It provides a comprehensive curriculum and learning path focused on data science, the development of predictive models, and the operational aspects of MLOps. The instructional material covers the full machine learning lifecycle, moving from basic data engineering to production deployment. This includes guides on wrapping models in APIs, utilizing container-based packaging, and implementing serverless architectures to host models in cloud environments. The program encompasses technical training in predic

This project is an educational resource and comprehensive guide for implementing and deploying deep learning models using the PyTorch framework. It provides a structured learning curriculum consisting of tutorials and notebooks that cover neural network architectures, data pipelines, and model optimization across multiple AI domains. The curriculum includes practical implementation guides for building convolutional networks, transformers, and recurrent models. It specifically focuses on workflows for computer vision, including image classification, object detection, and segmentation, as well

mmocr is a PyTorch-based optical character recognition framework designed for training and deploying text detection, recognition, and key information extraction models. It serves as a comprehensive toolbox for scene text detection and recognition, providing specialized libraries for locating text regions and converting visual text into machine-encoded strings. The project distinguishes itself through a research framework for key information extraction and advanced text spotting capabilities. These include point-based spotting using transformers and the use of parameterized Bezier curves to id

ccv is a computer vision library written in C designed for high-performance visual analysis. It serves as a framework for image classification, object detection, and the identification of faces, pedestrians, and vehicles. The library distinguishes itself through hardware-accelerated vision and deep learning inference optimizations. It utilizes a quantized tensor processor to transform floating-point data into eight-bit integers and implements integer-quantized attention mechanisms to reduce memory bandwidth and increase data throughput. The project covers a broad range of capabilities, inclu

This project is a PyTorch implementation of the YOLOv4 object detection framework. It provides a system for training and deploying neural networks that identify and locate multiple objects within images and video streams. The framework includes tools for converting trained weights into universal formats and hardware-specific optimized engines, specifically supporting ONNX and TensorRT. It features a TensorRT inference optimizer to reduce latency and increase throughput, as well as a model architecture compatible with NVIDIA DeepStream streaming analytics pipelines. The system covers model tr

Tensorpack is a high-level TensorFlow neural network framework and research library designed for building and training deep learning models. It provides a collection of reproducible neural network architectures for computer vision, generative tasks, reinforcement learning, and natural language processing. The project distinguishes itself through a specialized deep learning data pipeline that uses pure Python for parallel data loading and streaming. It includes a multi-GPU training orchestrator for distributing workloads via data-parallel strategies and a dedicated interpretability toolkit for

Nixtla is a time series analysis platform centered on a transformer-based foundation model. It provides zero-shot inference for forecasting and anomaly detection, allowing the system to predict future values for new time series without requiring model retraining. The project is designed for large-scale analysis, using distributed inference scaling and forecast parallelization to process millions of data series. It supports fine-tuning adaptation to adjust pretrained weights for domain-specific datasets and offers deployment options ranging from local execution and private containers to integr

Serving is a high-performance framework designed for deploying and scaling machine learning models as production services. It functions as a distributed inference engine that enables the execution of complex data processing workflows by chaining multiple models into directed acyclic graphs. The platform distinguishes itself through its ability to manage the entire production model lifecycle, allowing for hot-swappable versioning that updates services without downtime. It supports horizontal scaling through distributed model sharding and optimizes high-dimensional data retrieval via specialize