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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
Train a deep learning net with OpenStreetMap features and satellite imagery.
Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds
Deep Learning Papers on Medical Image Analysis
This repository contains code, network definitions and pre-trained models for working on remote sensing images using deep learning.
The main features of nshaud/deepnetsforeo are: Deep Learning Frameworks, Specialized Segmentation.
Projects with overlapping indexed features include: trailbehind/deeposm — Train a deep learning net with OpenStreetMap features and satellite imagery. nvidia/digits — DIGITS is a GPU deep learning training platform and model manager used to train, fine-tune, and manage neural network… mapbox/robosat — Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads,… albarqouni/deep-learning-for-medical-applications — Deep Learning Papers on Medical Image Analysis. amznlabs/amazon-dsstne — Amazon DSSTNE is a machine learning toolkit and sparse tensor network library designed for deep learning models with… alrojo/tensorflow-tutorial — Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE.