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

This project is a comprehensive educational resource and technical manual focused on interpretable machine learning and explainable AI. It serves as a textbook and reference for implementing techniques that make complex machine learning models transparent and understandable to humans. The resource provides guidance on both building inherently transparent models, such as decision trees and sparse linear models, and applying post-hoc explanation methods to black-box systems. It details specific methodologies for quantifying feature importance, generating rationales for individual predictions, a

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 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

lite.ai.toolkit एज AI तैनाती के लिए डिज़ाइन किया गया एक C++ कंप्यूटर विज़न टूलकिट है। यह संसाधन-सीमित उपकरणों पर ऑब्जेक्ट डिटेक्शन, इमेज क्लासिफिकेशन और सेगमेंटेशन के लिए प्री-ट्रेंड मॉडल के निष्पादन को सक्षम बनाता है। इस प्रोजेक्ट में एक मल्टी-बैकएंड इन्फरेंस इंजन है जो ONNX मॉडल रनटाइम का समर्थन करता है, जिससे AI मॉडल को विभिन्न हार्डवेयर लक्ष्यों पर चलने की अनुमति मिलती है। इसमें लेटेंसी को कम करने और प्रोसेसिंग गति बढ़ाने के लिए विशेष रूप से NVIDIA हार्डवेयर के लिए एक GPU-त्वरित पाइपलाइन शामिल है। यह टूलकिट चेहरे के विश्लेषण की क्षमताओं की एक विस्तृत श्रृंखला को कवर करता है, जिसमें भावना पहचान, लिंग और आयु अनुमान और हेड पोज़ विश्लेषण शामिल है। यह फीचर एम्बेडिंग के निष्कर्षण और पहचान को सत्यापित करने के लिए कोसाइन समानता की गणना के माध्यम से चेहरे की पहचान के लिए उपकरण भी प्रदान करता। अतिरिक्त क्षमताओं में फोरग्राउंड आइसोलेशन के लिए इमेज मैटिंग, ग्रेस्केल इमेज कलराइज़ेशन और आर्टिस्टिक स्टाइल ट्रांसफर शामिल हैं।

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