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Aprendizaje automático · GitHub

Explora herramientas para entrenar, evaluar y ejecutar modelos, y compara el interés de la comunidad.

Proyectos útiles de GitHub

7,586 proyectos

EPro-PnP

tjiiv-cprg

1,177estrellas
EPro-PnP preview

[CVPR 2022 Best Student Paper] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

571estrellas
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)

1,385estrellas

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

skrl

Toni-SM

1,104estrellas

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

TorchDR

TorchDR

217estrellas
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.

tsl

TorchSpatiotemporal

392estrellas

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.