Awesome GitHub RepositoriesSmall Object Detectors

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

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

GroundingDINO is a deep learning vision model and open-vocabulary object detector designed to map natural language prompts to spatial coordinates. It functions as a text-to-bounding-box framework that enables zero-shot image localization, allowing the system to identify and locate arbitrary objects without requiring predefined classes or specific training for those categories. The project distinguishes itself by matching visual features to natural language descriptions to achieve open-set visual recognition. It supports text-guided image localization and the isolation of specific objects base

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

VLM-R1 es un modelo de razonamiento visión-lenguaje y framework de IA corpórea (embodied AI) diseñado para mapear entradas visuales e instrucciones de lenguaje en puntos de navegación físicos y acciones robóticas. Funciona como un optimizador de políticas multimodal y un detector de vocabulario abierto capaz de localizar objetos basados en descripciones arbitrarias en lenguaje natural. El sistema se distingue por el uso de razonamiento de cadena de pensamiento (chain-of-thought) y aprendizaje por refuerzo para resolver tareas visuales y espaciales complejas. Utiliza un sistema de memoria semántica de video, que emplea una caché visual para mantener un historial de video en vivo para interacciones de baja latencia y razonamiento temporal continuo. El framework cubre una amplia gama de capacidades, incluyendo el mapeo de puntos de navegación monoculares para robótica, la localización de tokens de región para identificación de objetos y el ajuste fino supervisado basado en políticas para la estabilidad del razonamiento multimodal. También admite detección de vocabulario abierto, comprensión de expresiones de referencia y la extracción de características de objetos detalladas mediante recuperación de prompts visuales. El proyecto está implementado en Python y admite inferencia en hardware Ascend.