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Train, evaluate, and run machine learning models.

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fire-detection-cnn

tobybreckon

571stars
fire-detection-cnn preview

real-time fire detection in video imagery using a convolutional neural network (deep learning) - from our ICIP 2018 paper (Dunnings / Breckon) + ICMLA 2019 paper (Samarth / Bhowmik / Breckon)

Machine LearningPythonMITGitHub

Technical_Book_DL

tomepel

1,385stars

This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent.

Machine LearningTeXGitHub

skrl

Toni-SM

1,104stars

Modular Reinforcement Learning (RL) library (implemented in PyTorch, JAX, and NVIDIA Warp) with support for Gymnasium/Gym, NVIDIA Isaac Lab, MuJoCo Playground and other environments

Machine LearningPythonMITGitHub

TorchDR

TorchDR

217stars
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.
Machine LearningPythonBSD-3-ClauseGitHub

Ensemble-Pytorch

TorchEnsemble-Community

1,038stars

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

Machine LearningPythonBSD-3-ClauseGitHub

tsl

TorchSpatiotemporal

392stars

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.
Machine LearningPythonMITGitHub

torchsynth

torchsynth

388stars

A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.

Machine LearningPythonApache-2.0GitHub

crfasrnn

torrvision

1,334stars
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/

Machine LearningMATLABGitHub

examples

towhee-io

517stars

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

Machine LearningJupyter NotebookApache-2.0GitHub

birda

tphakala

43stars

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
Machine LearningRustMITGitHub

tmrl

trackmania-rl

750stars
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.
Machine LearningPythonMITGitHub