Code release for ConvNeXt model
Les fonctionnalités principales de facebookresearch/convnext sont : Image Classification Models, Convolutional Neural Networks, Depthwise Separable Convolutions, Hierarchical Stage Architectures, Overlap Patch Embeddings, Pre-Activation Residual Blocks, Learnable Scaling Layers, Global Response Normalizers.
Les alternatives open-source à facebookresearch/convnext incluent : kaiminghe/deep-residual-networks — This project provides a deep residual network framework and pre-trained PyTorch models designed for high-accuracy… fchollet/deep-learning-models — This project is a collection of deep learning tools for image classification and audio tagging, providing a repository… paddlepaddle/paddledetection — PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of… tingsongyu/pytorch-tutorial-2nd — This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It… ultralytics/ultralytics — Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep… ultralytics/yolov5 — YOLOv5 is a comprehensive computer vision framework designed for end-to-end deep learning, specializing in real-time…
This project provides a deep residual network framework and pre-trained PyTorch models designed for high-accuracy image recognition. It implements a neural network architecture that utilizes skip connections to enable the training of very deep models without gradient degradation. The system is designed for computer vision tasks, including image classification, object detection, and visual data segmentation. It includes weights trained on ImageNet to support transfer learning and the fine-tuning of models on custom image datasets. The architectural design focuses on residual learning blocks,
This project is a collection of deep learning tools for image classification and audio tagging, providing a repository of pre-trained model weights and architectures. It serves as a Keras model zoo that enables the immediate use of established neural networks for inference and transfer learning. The library includes a music tagging framework that classifies audio recordings using convolutional recurrent neural networks and mel-spectrograms. For visual data, it provides implementations of architectures such as ResNet, VGG, and Xception, alongside a repository of weights trained on large datase
PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti
This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It covers the fundamental building blocks of deep learning, including tensor manipulation, automatic differentiation, and the construction of modular neural network components. The repository serves as a technical guide for several specialized domains. It provides implementation details for computer vision tasks such as image classification, object detection, and semantic segmentation, as well as natural language processing workflows involving transformers, recurrent networks, and gen