Open-source alternatives to Robosat

DIGITS is a GPU deep learning training platform and model manager used to train, fine-tune, and manage neural network models on NVIDIA hardware. It functions as a REST-controlled machine learning pipeline that integrates with S3 cloud storage for dataset ingestion and organization. The platform supports image classification workflows, allowing users to train various model architectures and export trained image classifiers for use in external environments. It includes capabilities for model fine-tuning to adapt pretrained weights to specific tasks. The system provides a REST-based API interfa

Segment Geospatial is a Python toolkit for isolating geographic features in remote sensing imagery using the Segment Anything Model. It functions as a remote sensing image processor that converts map tiles into georeferenced formats to generate segmentation masks from satellite data. The system enables the extraction of geographic objects through automatic mask generation or manual prompts, such as text descriptions, bounding boxes, and interactive markers. It supports timeseries imagery segmentation to track or identify objects across sequences of images over different dates and provides a g

YOLOv5 is a comprehensive computer vision framework designed for end-to-end deep learning, specializing in real-time object detection, image classification, and instance segmentation. It provides a unified toolkit that manages the entire lifecycle of a model, from initial dataset configuration and hyperparameter tuning to high-speed inference and deployment. The framework utilizes a modular neural architecture, allowing users to swap backbone and head components to tailor models for specific visual tasks. What distinguishes this project is its focus on production-ready deployment and model ef

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

You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery

TorchSat is an open-source deep learning framework for satellite imagery analysis based on PyTorch.

TorchGeo is a PyTorch library designed for deep learning on geospatial data, providing a framework for building and training neural networks for tasks such as semantic segmentation, object detection, and change detection. It serves as a comprehensive pipeline for remote sensing, featuring specialized dataset loaders and multispectral image preprocessing tools. The library is distinguished by a dedicated remote sensing model zoo and extensive support for transfer learning, allowing users to integrate pre-trained weights optimized for specific satellite sensors. It also includes support for sel

MathUtilities is a collection of specialized toolkits providing engines for geometry, computer vision, mathematics, physics simulation, and signal processing. It functions as a comprehensive mathematics and physics library focused on linear algebra, numerical optimization, and geometric calculations for technical applications. The project distinguishes itself through a physics simulation toolkit and a 3D geometry engine. These provide capabilities for Verlet integration, iterative inverse kinematics solvers, distance field rendering via volumetric raymarching, and mesh geometry deformation. I

This repository is a collection of practical machine learning implementations designed to demonstrate core predictive analytics, computer vision, and natural language processing techniques. It serves as a resource for applying standard machine learning frameworks to solve diverse data science problems, ranging from automated classification to complex pattern recognition. The project distinguishes itself by providing concrete examples across multiple domains, including the development of conversational interfaces, the analysis of geospatial data, and the implementation of deep learning archite

Amazon DSSTNE is a machine learning toolkit and sparse tensor network library designed for deep learning models with sparse inputs and outputs. It provides a model-parallel training framework and a GPU-accelerated sparse engine to support memory-intensive networks. The framework is specifically designed for recommendation system training and large-scale sparse learning. It enables the distribution of large weight matrices and embedding tables across multiple GPU devices to handle models that exceed the memory capacity of a single processor. The project covers a broad range of capabilities in

Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE

Albumentations is a computer vision image augmentation library designed to increase training data diversity for deep learning models. It provides a toolset for applying geometric and color transformations to images and annotations, including a specialized collection of 3D operations for volumetric data used in medical and scientific imaging. The library functions as an image mask and bounding box transformer, automatically updating masks, bounding boxes, and keypoints when images undergo geometric changes. This ensures that spatial alterations remain synchronized across images and their assoc

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

This repository serves as a structured educational resource for machine learning and deep learning, providing a library of executable scripts and notebooks. It is designed to help users master the practical application of data processing, model evaluation, and neural network construction through annotated code samples and guided tutorials. The collection focuses on translating theoretical mathematical concepts into functional code, offering proven patterns for common tasks such as classification and regression. By providing curated examples of layer construction and training loops, the reposi

3D Computer Vision Framework

Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for deep learning models. It provides a collection of transformations that modify pixel values and spatial geometry to increase the diversity of training samples and improve model generalization. The library supports both 2D image augmentation and 3D volumetric data augmentation. It handles a variety of labels alongside images, ensuring that bounding boxes, keypoints, and segmentation masks remain accurately aligned when spatial transformations are applied. The tool incorporates

Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/

A PyTorch implementation of the YOLO v3 object detection algorithm

This repository contains examples of using Raster Vision on open datasets.

Paddle is a deep learning framework designed for building, training, and deploying large-scale machine learning models. It incorporates a distributed training engine for optimizing performance across multiple chips and a model inference engine for transforming trained models into production-ready formats for cross-platform execution. The platform features a heterogeneous hardware abstraction and a standardized software stack that allows models to run across diverse hardware architectures through a common interface. It also includes a scientific computing library capable of solving complex dif

Wrapper library for text generation / language models at character and word level with RNNs in TensorFlow

The Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN) codebase combines some of the leading object detection algorithms into a unified framework designed to detect objects both large and small in overhead imagery. This work seeks to extend the YOLT modification of…