Awesome GitHub RepositoriesSegmentation Model Training

Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification. By utilizing a modular architecture, the platform allows users to swap model components to balance inference speed and accuracy requirements for diverse applications. The framework distinguishes itself through its support for real-time processing and flexible deployment. It in

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

This project is a comprehensive deep learning framework and educational platform designed for constructing, training, and evaluating neural network architectures. It provides a modular environment for building models through tensor operations and automatic differentiation, supporting a wide range of tasks from image classification and object detection to sequential data processing. Beyond its core technical capabilities, the project distinguishes itself by integrating professional career development resources directly into its learning ecosystem. It offers structured guidance, resume reviews,

SentencePiece is a text segmentation engine and tokenization library designed for machine learning workflows. It provides a comprehensive toolkit for transforming raw text into subword units or numerical identifiers, enabling consistent data representation for neural network training and inference. The library supports the training of segmentation models from raw text, allowing for the creation of custom vocabularies tailored to specific domain requirements. The project distinguishes itself through its byte-level encoding and fallback mechanisms, which ensure that every input can be represent

Pytorch-UNet is a deep learning implementation designed for semantic image segmentation. It provides a framework for training convolutional neural networks to perform pixel-wise classification, transforming input images into detailed prediction masks. The project utilizes a symmetric encoder-decoder architecture that employs skip-connection feature fusion to recover fine-grained boundary details. It includes support for mixed-precision training to reduce memory usage and accelerate processing speeds. The framework covers the end-to-end segmentation pipeline, from model training using custom

InternVL is a vision-language model framework that fuses a visual encoder with a large language model to translate image features into textual tokens for reasoning. It provides a system for multimodal inference and dialogue, enabling the processing of images and text to answer questions or generate descriptions. The project is distinguished by its high-resolution image processing, which uses dynamic tiling to maintain detail for images up to 4K resolution, and its chain-of-thought visual reasoning for solving complex mathematical and spatial problems. It also supports temporal frame sampling

AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc

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

MMSegmentation is an open-source semantic segmentation toolbox built on PyTorch that provides a modular, configurable framework for building, training, evaluating, and deploying segmentation models. At its core, it offers a config-driven pipeline that assembles training, evaluation, and inference workflows by parsing hierarchical configuration files, with a modular component registry that enables plug-and-play composition of neural network modules, optimizers, datasets, and metrics. The framework supports the full model lifecycle through a unified runner interface that controls training, testi

This project is a modular PyTorch framework for training and evaluating object detection and instance segmentation models. It serves as a computer vision research tool and a deep learning inference engine designed to identify object locations, classes, and pixel-level masks within images. The framework implements a two-stage inference pipeline that utilizes region proposal networks and a symmetric mask-head architecture. It provides specialized capabilities for instance segmentation, object bounding box detection, and human pose estimation via anatomical keypoint detection. The system includ

FastSAM is an image segmentation framework that uses convolutional neural networks to isolate visual elements and generate masks for detectable objects within images. It provides a system for both automatic all-object segmentation and promptable image segmentation. The project utilizes an inference-optimized architecture to reduce computational overhead, enabling faster mask generation and real-time visual analysis. It supports the creation of precise masks through various prompt inputs, including points, bounding boxes, and text descriptions. The framework covers broader computer vision cap

nnU-Net is a PyTorch-based deep learning framework for the supervised semantic segmentation of 2D and 3D biomedical images. It functions as an automated medical imaging pipeline that generates predicted masks and labels from clinical images. The system distinguishes itself by using dataset-driven auto-configuration to automatically select the optimal network architecture, preprocessing steps, and training hyperparameters based on the specific properties of the input medical dataset. The framework covers a broad range of capabilities including medical dataset preparation, intensity normalizat

SPADE is a semantic image synthesis framework and generative adversarial network designed to transform semantic label maps into photorealistic images. It uses a spatially-adaptive normalization model to modulate activations based on semantic maps, ensuring that spatial layouts and details are preserved throughout the synthesis process. The project enables the generation of diverse image variations from a single semantic layout by integrating variational autoencoders and latent vector style control. These mechanisms allow for the adjustment of visual appearances and textures while keeping the

SAMURAI is a zero-shot visual tracking model that adapts the Segment Anything architecture for video object segmentation. It uses a first-frame prompt, such as a bounding box or mask, to initialize tracking, then employs a motion-aware memory mechanism that stores and updates temporal motion features across frames to guide mask refinement. An online memory update strategy continuously refreshes this memory with new frame predictions, while temporal motion encoding computes optical flow between consecutive frames to inform object boundary and occlusion handling. The system is designed for real

PaddleX is a PaddlePaddle-based framework for building, deploying, and fine-tuning AI model pipelines, with pre-built support for computer vision, OCR, document analysis, and time series tasks. It offers a toolkit of ready-to-use pipelines for image classification, object detection, segmentation, and pose estimation, alongside an end-to-end OCR document analysis pipeline that extracts text, tables, formulas, and layout information. The platform also includes a dedicated time series forecasting pipeline for analyzing historical data to detect anomalies, classify patterns, and predict future val

RF-DETR is a Python library for training and deploying object detection, instance segmentation, and keypoint detection models built on a vision transformer architecture. It provides a unified command-line interface and Python API for the full workflow, from fine-tuning pretrained checkpoints on custom datasets to running inference on images, video files, and live camera streams. The project supports training on datasets in COCO or YOLO format, with automatic format detection and configurable augmentation pipelines. Models can be exported to ONNX, TFLite, or TensorRT for deployment across edge

Yolact es un framework de visión artificial y modelo de segmentación de instancias en tiempo real. Utiliza una red neuronal totalmente convolucional para detectar objetos y generar máscaras a nivel de píxel para imágenes y flujos de video. El sistema emplea la generación de máscaras prototípicas para crear prototipos de máscara globales que se combinan linealmente para obtener resultados específicos de instancia. Incorpora capas convolucionales deformables y agrupación de regiones de interés (RoI) deformable para adaptar el muestreo espacial a las formas irregulares de los objetos. El framework cubre el ciclo de vida completo de desarrollo del modelo, incluyendo el entrenamiento en conjuntos de datos personalizados, evaluación de precisión mediante precisión media promedio (mAP) y el uso de entrenamiento distribuido multi-GPU para escalar la velocidad de procesamiento. También proporciona utilidades de procesamiento de medios para aplicar máscaras de segmentación a imágenes y exportar archivos de video anotados. El proyecto incluye herramientas de persistencia de estado para la gestión de puntos de control y reanudación del entrenamiento, junto con registro para grabar métricas y valores de pérdida.

Augmentor es una librería y framework de aumento de imágenes en Python diseñado para expandir datasets de machine learning. Funciona como una herramienta de preprocesamiento que genera variaciones sintéticas de imágenes para aumentar la diversidad de datos y como un streamer de datos de entrenamiento que alimenta imágenes y etiquetas aumentadas directamente en bucles de redes neuronales sin requerir almacenamiento intermedio en disco. El framework mantiene la alineación espacial entre las imágenes y sus máscaras correspondientes, lo cual es necesario para el entrenamiento de segmentación semántica. Soporta varias transformaciones geométricas y a nivel de píxel, incluyendo distorsiones elásticas, cambios de perspectiva mediante inclinación y sesgo, rotación, cizallamiento y borrado aleatorio de regiones. El sistema incluye capacidades para estrategias de procesamiento por clase para abordar el desequilibrio de datos y utiliza multi-threading para acelerar la generación paralela de datasets aumentados. También proporciona utilidades para limpiar y estandarizar archivos de imagen crudos durante la etapa de preprocesamiento.

Este proyecto es una implementación en PyTorch de una red neuronal convolucional U-Net diseñada para la segmentación de imágenes a nivel de píxel. Funciona como un procesador de imágenes biomédicas que genera máscaras precisas para aislar estructuras anatómicas dentro de imágenes médicas. La arquitectura utiliza una estructura simétrica de codificador-decodificador para capturar el contexto y permitir una localización precisa. Emplea fusión de características mediante conexiones de salto (skip-connections) para combinar características de alta resolución de la ruta de contracción con salidas muestreadas hacia arriba, recuperando detalles espaciales. El sistema cubre el entrenamiento de modelos de deep learning utilizando pérdida de entropía cruzada binaria e incorpora técnicas de aumento de datos, como interpolación cúbica, rotación y volteo, para ampliar la variedad del conjunto de datos de entrenamiento. La implementación se proporciona como un Jupyter Notebook.

Este repositorio es un programa educativo integral y un framework de deep learning diseñado para enseñar aprendizaje profundo práctico usando PyTorch a través de notebooks y ejemplos de código. Sirve como una librería de alto nivel para construir, entrenar y desplegar redes neuronales, actuando como un orquestador de entrenamiento de modelos que coordina modelos de PyTorch, optimizadores y funciones de pérdida. El proyecto proporciona kits de herramientas especializados para visión artificial, procesamiento de lenguaje natural y preprocesamiento de datos tabulares. Se distingue por controles de entrenamiento avanzados como tasas de aprendizaje discriminativas, un sistema de callbacks bidireccional para personalizar la lógica de entrenamiento y una abstracción de learner de alto nivel que automatiza la colocación en dispositivos y los bucles de entrenamiento. El framework cubre una amplia superficie de capacidades, incluyendo la construcción automatizada de pipelines de datos, análisis de arquitectura de modelos y evaluación de rendimiento en tareas de clasificación, regresión y segmentación. También incluye utilidades para entrenamiento distribuido en múltiples GPUs, entrenamiento de precisión mixta para optimización de memoria y soporte especializado para datos de imágenes médicas. El proyecto se entrega como una serie de Jupyter Notebooks.