Awesome GitHub RepositoriesImage Classifiers

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

react-native-firebase is a modular set of libraries that integrates Firebase cloud services into cross-platform mobile applications. It serves as a native-SDK wrapper, mapping JavaScript method calls to native iOS and Android Firebase SDKs via the React Native bridge to provide a type-safe interface for mobile backend integration. The project enables connectivity to a wide array of cloud services, including user authentication and identity management, NoSQL cloud databases with real-time synchronization, and scalable cloud storage for media files. It also provides tools for sending push notif

This project is an agnostic model interpretability framework and explainability tool designed to provide local interpretable explanations for individual predictions. It functions as a local surrogate model that approximates the behavior of any machine learning classifier or regression model to identify the most influential features for a specific instance. The framework is designed to be model-agnostic, meaning it can explain predictions across tabular, text, and image data regardless of the underlying architecture. It employs local linear approximations and feature importance visualization t

This project is an automated machine learning framework and toolkit designed for training and tuning custom models for classification, regression, and recommendations. It functions as a multimodal machine learning toolkit capable of processing and training models using a combination of text, image, audio, and sensor data. The framework distinguishes itself as a multimodal data processor that can handle and visualize large datasets on a single machine using column-oriented disk storage. It includes a core machine learning model generator that converts trained models into formats compatible wit

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

This project is a high-performance image transformation server and media optimization proxy designed to process, resize, and convert assets on the fly. It functions as a secure pipeline that fetches remote source files and applies transformations—such as cropping, watermarking, and visual filtering—directly through parameters defined in the request URL. The service distinguishes itself through a focus on secure, resource-aware delivery. It protects infrastructure by validating incoming requests with cryptographic signatures to prevent unauthorized access and enforces strict limits on file dim

YOLOv9 is a real-time computer vision framework and deep learning model designed for image classification, object detection, and instance segmentation. It functions as both a vision model and a trainer, allowing for the optimization of neural network weights on custom datasets using single or multiple GPUs. The framework utilizes programmable gradient information to perform high-speed identification and location of multiple objects within images and video streams. It extends beyond bounding box detection to provide instance segmentation and panoptic segmentation, which labels every pixel in a

NSFW detection on the client-side via TensorFlow.js

This is a classifier-guided diffusion framework for high-fidelity image generation. It implements a cascaded diffusion pipeline that chains a base diffusion model with a dedicated upsampler to progressively increase image resolution in stages, and uses classifier-guided diffusion sampling to steer the reverse diffusion process toward higher-quality outputs. The framework provides tools for training diffusion models from scratch using distributed processes with gradient accumulation, as well as training classifier models that provide gradient-based guidance during sampling. It supports both un

Corenet is a deep learning training framework and computer vision model library designed for developing neural networks across vision, text, and audio modalities. It functions as a distributed training orchestrator for scaling workloads across multiple compute nodes and provides a multimodal data pipeline for processing image, text, and video data. The project includes a model conversion toolkit for transforming weights and architectures between different machine learning frameworks. It also provides tools for optimizing model performance on Apple Silicon and reducing response latency in gene

ml5-library is a JavaScript machine learning library that functions as a browser-based inference engine. It provides a high-level wrapper for implementing neural networks and data models, allowing users to execute machine learning predictions directly on the client side. The library simplifies the integration of machine learning into web applications and creative coding projects by removing the requirement for deep mathematical expertise. It specifically enables web-based image classification through the use of pretrained deep learning models to identify and label objects within images. The

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

Este proyecto es un recurso educativo integral y manual técnico centrado en el machine learning interpretable y la IA explicable. Sirve como libro de texto y referencia para implementar técnicas que hacen que los modelos de machine learning complejos sean transparentes y comprensibles para los humanos. El recurso proporciona orientación tanto sobre la construcción de modelos inherentemente transparentes, como árboles de decisión y modelos lineales dispersos, como sobre la aplicación de métodos de explicación post-hoc a sistemas de caja negra. Detalla metodologías específicas para cuantificar la importancia de las características, generar fundamentos para predicciones individuales y utilizar modelos sustitutos para aproximar procesos complejos de toma de decisiones. El contenido cubre una amplia gama de capacidades analíticas, incluyendo el análisis de influencia de características globales y locales, la interpretabilidad de visión artificial y el uso de contribuciones de teoría de juegos como los valores de Shapley. También aborda la evaluación de modelos mediante evaluaciones de interpretabilidad, flujos de trabajo de depuración para identificar atajos de modelos y el diseño de estructuras de algoritmos transparentes. El proyecto se implementa como una colección de Jupyter Notebooks.

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

Deep Java Library es un framework de deep learning para Java y motor de inferencia de modelos para la JVM. Proporciona una API de alto nivel para construir y desplegar modelos de deep learning dentro del ecosistema Java, actuando como un runtime multiplataforma para ejecutar modelos en CPUs, GPUs y dispositivos móviles. La librería es agnóstica al motor, permitiendo a los usuarios cambiar entre diferentes motores de deep learning como PyTorch, TensorFlow y MXNet mientras mantienen una única API unificada. Esto permite el despliegue del mismo modelo en diferentes backends sin cambiar el código de la aplicación. El framework soporta el ciclo de vida completo del machine learning, incluyendo la construcción y entrenamiento de arquitecturas de redes neuronales y la ejecución de inferencia en tiempo real. Incluye capacidades para la inferencia de machine learning distribuida para escalar predicciones a través de tuberías de big data y la capacidad de desplegar modelos como microservicios o dentro de aplicaciones cliente. El sistema cubre una amplia gama de dominios, incluyendo visión por computadora para detección de rostros y clasificación de imágenes, y procesamiento de lenguaje natural para análisis de sentimiento y embeddings de oraciones.

lite.ai.toolkit es un toolkit de visión artificial en C++ diseñado para el despliegue de IA en el borde (edge). Permite la ejecución de modelos preentrenados para detección de objetos, clasificación de imágenes y segmentación en dispositivos con recursos limitados. El proyecto cuenta con un motor de inferencia multi-backend que admite el runtime de modelos ONNX, permitiendo que los modelos de IA se ejecuten en diferentes objetivos de hardware. Incluye un pipeline acelerado por GPU específicamente para hardware NVIDIA para reducir la latencia y aumentar la velocidad de procesamiento. El toolkit cubre una amplia gama de capacidades de análisis facial, incluyendo detección de emociones, estimación de género y edad, y análisis de pose de cabeza. También proporciona herramientas para el reconocimiento facial mediante la extracción de embeddings de características y el cálculo de similitud de coseno para verificar identidades. Las capacidades adicionales incluyen el matting de imágenes para el aislamiento del primer plano, la colorización de imágenes en escala de grises y la transferencia de estilo artístico.

This project is a computer vision dataset and image annotation repository designed for training and evaluating machine learning models. It provides a large collection of labeled images, serving as an object detection benchmark and a source of pixel-level segmentation data. The repository distinguishes itself as a multimodal visual dataset by pairing images with synchronized voice, text, and mouse traces to support narrative understanding. It further enables the analysis of model fairness through the inclusion of demographic attributes and exhaustive annotations. The dataset covers a broad ra

This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen