Fully Convolutional Networks (FCN)
The foundational architecture for end-to-end, pixel-wise semantic segmentation and dense visual prediction.
Updated 11d ago
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Semantic Segmentation
Object Localization
Boundary Detection
GluonTS
4.6
Free Users praise GluonTS for its flexibility, comprehensive feature set, and strong support for deep learning models.
GluonTS is a Python toolkit for probabilistic time series modeling, focused on deep learning.
Free pricingAPI availableUpdated 2026-03-17
Best fits
Time Series ForecastingProbabilistic Modeling
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About GluonTS
GluonTS is a Python toolkit dedicated to probabilistic time series modeling, with a strong emphasis on deep learning-based approaches. It provides a comprehensive suite of tools for time series data manipulation, model building, training, and evaluation. The library supports various forecasting models, including DeepAR, DeepState, and Transformer-based models. GluonTS uses PyTorch and MXNet as backend frameworks, offering flexibility in model implementation and customization. Key features include pre-built datasets, data splitting utilities, synthetic data generation, and customizable loss functions. GluonTS facilitates the development of custom models through PyTorch, tuning with Optuna, and using trainer callbacks. The core value lies in providing a unified platform for developing and deploying advanced time series forecasting solutions.
Core Capabilities
GluonTS is a Python toolkit dedicated to probabilistic time series modeling, with a strong emphasis on deep learning-based approaches.
Main Tasks
Develop deep learning models
Explore all tools that specialize in develop deep learning models. This domain focus ensures GluonTS delivers optimized results for this specific requirement.
