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Maschinelles Lernen · GitHub

Finde Frameworks zum Trainieren, Bewerten und Ausführen von Modellen und vergleiche das Community-Interesse.

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7,585 Projekte

TorchDR

TorchDR

217Sterne
TorchDR preview

TorchDR - PyTorch Dimensionality Reduction

  • High Performance - Engineered for speed with GPU acceleration, torch.compile support, and optimized algorithms leveraging sparsity and negative sampling.
  • Multi-GPU Support - Scale to massive datasets with built-in distributed computing. Use the torchdr CLI or torchrun for easy multi-GPU execution of compatible modules and methods.
Maschinelles LernenPythonBSD-3-ClauseGitHub

tsl

TorchSpatiotemporal

392Sterne

tsl: a PyTorch library for processing spatiotemporal data.

  • Create Custom Models and Datasets Easily build your own custom models and datasets for spatiotemporal data analysis. Whether you're working with sensor networks, environmental data, or any other spatiotemporal domain, tsl's high-level APIs empower you to develop tailored solutions.
  • Access a Wealth of Existing Datasets and Models Leverage a vast collection of datasets and models from the spatiotemporal data processing literature. Explore and benchmark against state-of-the-art baselines, and test your brand new model on widely used public datasets.
Maschinelles LernenPythonMITGitHub

crfasrnn

torrvision

1,334Sterne
crfasrnn preview

This repository contains the source code for the semantic image segmentation method described in the ICCV 2015 paper: Conditional Random Fields as Recurrent Neural Networks. http://crfasrnn.torr.vision/

Maschinelles LernenMATLABGitHub

examples

towhee-io

517Sterne

Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.

Maschinelles LernenJupyter NotebookApache-2.0GitHub

birda

tphakala

43Sterne

Fast CLI tool for bird species detection using BirdNET and Perch AI models

  • Multiple AI Models: Support for BirdNET v2.4, BirdNET v3.0, Google Perch v2, BSG Finnish Birds, and BattyBirdNET bat classifiers
  • GPU Acceleration: Optional CUDA support for faster inference on NVIDIA GPUs
Maschinelles LernenRustMITGitHub

tmrl

trackmania-rl

750Sterne
tmrl preview

Reinforcement Learning for real-time applications

  • Analog control from screenshots: The tmrl example pipeline trains policies that are able to drive from raw screenshots captured in real-time. For beginners, we also provide simpler rangefinder ("LIDAR") observations, which are less potent but easier to learn from. The example pipeline controls the game via a virtual gamepad, which enables analog actions.
  • Models: To process LIDAR measurements, the example tmrl pipeline uses a Multi-Layer Perceptron (MLP). To process raw camera images (snapshots), it uses a Convolutional Neural Network (CNN). These models learn the physics of the game from histories or observations equally spaced in time.
Maschinelles LernenPythonMITGitHub