Projects sharing features with Satellite Image Deep Learning

This project is a comprehensive, community-driven repository that serves as a centralized catalog for computer vision research and development. It functions as a structured index of academic papers, open-source software libraries, public datasets, and educational tutorials, providing a navigation point for the complex landscape of modern vision technology. The repository distinguishes itself through a taxonomy-based indexing system that maps the relationships between foundational research, influential academic figures, and their corresponding software implementations. By utilizing a lightweig

This project is a comprehensive computer vision library for the PyTorch ecosystem, providing a standardized collection of neural network architectures, datasets, and high-performance transformation utilities. It serves as a foundational framework for building, training, and deploying deep learning models, offering a centralized model registry that allows developers to instantiate architectures with pre-trained weights for tasks such as image classification, object detection, and semantic segmentation. The library distinguishes itself through its modular approach to data and compute management

This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

OpenCV is a comprehensive computer vision library designed for real-time performance and cross-platform deployment. It provides a native execution environment that leverages multi-threaded operations and automated memory management to handle intensive computational tasks, including image processing and machine learning model inference. The library distinguishes itself through a data-oriented matrix framework that utilizes proxy-based array abstractions to provide a consistent interface for multidimensional data. By employing factory-pattern algorithm interfaces and runtime type dispatching, i

Library for Fast and Flexible Human Pose Estimation

Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

Real-Time 3D Semantic Reconstruction from 2D data

An open source tool to quantify the world

Fawkes is an adversarial image generator and facial recognition cloaking tool designed to protect privacy by obfuscating facial features in photos. It functions as an image privacy obfuscator that adds invisible pixel perturbations to images, preventing facial recognition models from accurately identifying a person while keeping the image visually clear to humans. The system employs adversarial perturbation mapping and feature-space obfuscation to mislead machine learning classifiers. By utilizing an iterative optimization loop and model-agnostic noise generation, it modifies facial represent

pysot is a computer vision framework designed for single object tracking. It provides a platform for implementing and evaluating algorithms that locate and follow specific target objects across sequences of video frames. The project includes implementations of the SiamRPN architecture for region proposal network based localization and the SiamMask model, which combines tracking with binary mask generation to provide pixel-level segmentation of objects. The framework also contains a visual tracking evaluation toolkit used to measure the accuracy and reliability of tracking algorithms against

An unsupervised learning framework for depth and ego-motion estimation from monocular videos

EasyOCR is a deep learning-based computer vision library designed to perform optical character recognition on images and video frames. It functions as a comprehensive pipeline that automates the transformation of visual text into machine-readable strings, enabling the digitization of physical documents, forms, and receipts into searchable data. The engine distinguishes itself through a multi-stage processing workflow that combines convolutional neural networks for spatial feature extraction with sequence-based decoding mechanisms. This architecture allows the system to identify and interpret

imgaug is a Python library for machine learning data augmentation and computer vision dataset expansion. It provides tools to increase the volume and variety of training sets by applying random geometric, color, and noise transformations to images. The library ensures spatial consistency by synchronizing transformations across images and their associated annotations, such as bounding boxes, keypoints, and segmentation maps. It uses a compositional pipeline pattern to chain multiple augmentations into sequences and employs deterministic seed management to reproduce specific data samples. The

Find-Object project

Libvips is a C-based image processing library designed to manipulate large visual assets through a low-memory, parallel processing pipeline. It functions as a streaming image processor that avoids loading entire files into system memory, enabling the handling of massive images in resource-constrained environments. The library distinguishes itself through a demand-driven architecture that constructs a deferred execution plan, computing only the necessary pixels for a final output. By utilizing a cache-friendly tiled processing model and memory-mapped file access, it minimizes latency and redun

Detectron2 is a PyTorch computer vision framework and visual recognition platform designed for training and deploying models for object detection, image segmentation, and visual recognition. It provides a research-oriented environment for training complex vision models with multi-GPU acceleration. The project includes a specialized object detection library for identifying and locating multiple objects via bounding boxes, as well as an image segmentation toolkit for creating pixel-level masks through instance, semantic, and panoptic segmentation. Additionally, it features a human pose estimati

The implementation of an algorithm presented in the CVPR18 paper: "Detect-and-Track: Efficient Pose Estimation in Videos"

Yolact is a computer vision framework and real-time instance segmentation model. It utilizes a fully convolutional neural network to detect objects and generate pixel-level masks for images and video feeds. The system employs prototypical mask generation to create global mask prototypes that are linearly combined for instance-specific results. It incorporates deformable convolutional layers and deformable region-of-interest pooling to adapt spatial sampling to the irregular shapes of objects. The framework covers the full model development lifecycle, including training on custom datasets, ac

C++ image processing and machine learning library with using of SIMD: SSE, AVX, AVX-512, AMX for x86/x64, NEON, SVE for ARM, HVX for Hexagon

3D Computer Vision Framework

Meshroom is a node-based photogrammetry software designed to transform collections of two-dimensional images into three-dimensional models and scene geometry. It provides a visual interface for constructing and managing modular data pipelines, allowing users to automate complex computer vision tasks such as feature extraction, depth map estimation, and mesh generation. The software distinguishes itself through a distributed computational framework that dispatches resource-intensive tasks across local hardware or remote render farms. By utilizing a directed acyclic graph execution model, it en

Python package for earth-observing satellite data processing

Earth observation processing framework for machine learning in Python

TensorFlow Implementation for Computing a Semantically Segmented Bird's Eye View (BEV) Image Given the Images of Multiple Vehicle-Mounted Cameras.

Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds