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The main features of adambielski/capsnet-pytorch are: Model Implementations.
Projects with overlapping indexed features include: meta-llama/llama-cookbook — This project is a collection of implementation guides, recipes, and developer resources for building applications with… aaron-xichen/pytorch-playground — Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet,… adap/flower — Flower is a federated learning framework and distributed machine learning orchestrator designed to train models across… agermanidis/thingscoop — Search and filter videos based on objects that appear in them using convolutional neural networks. ahirner/pytorch-retraining. 1zb/deformable-convolution-pytorch — PyTorch implementation of Deformable Convolution.
This project is a collection of implementation guides, recipes, and developer resources for building applications with Llama models. It serves as a comprehensive kit for developing autonomous agents, establishing retrieval-augmented generation systems, and executing model fine-tuning. The resource provides specific patterns for multimodal workflows that process text, images, and audio. It includes specialized guidance on adapting pre-trained model weights for targeted tasks and implementing tool-calling orchestration to connect models with external APIs and functions. The codebase covers a b
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
Flower is a federated learning framework and distributed machine learning orchestrator designed to train models across decentralized devices. It functions as a privacy-preserving toolkit that enables model training and data analysis on local hardware, ensuring raw data remains on the device while contributing to a synchronized global model. The system employs an agnostic wrapper and integrator to connect diverse machine learning libraries, allowing different frameworks to operate within the same training loop. It uses a remote procedure call orchestrator to manage the exchange of model weight
PyTorch implementation of Deformable Convolution