Projects sharing features with TernausNetV2

Mask RCNN 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…

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

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

Keras implementation of Deeplab v3+ with pretrained weights

Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying neural networks across distributed systems, mobile devices, and hardware accelerators. It functions as a cross-platform runtime and a dynamic dataflow scheduler that optimizes neural network execution. The framework provides a multi-language API, enabling the development of machine learning models using Python, R, Julia, Scala, Go, and JavaScript. It supports high-performance model training and the scaling of workloads across multiple GPUs and machines. The system covers cap

CosmiQ Works Geospatial Machine Learning Analysis Toolkit

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

Data Preparation for Satellite Machine Learning

Implementation of various Deep Image Segmentation models in keras.

DeepLab-ResNet rebuilt in TensorFlow