DenseNet Implementation in Tensorflow
The main features of yixuanli/densenet-tensorflow are: Image Classification Architectures.
Open-source alternatives to yixuanli/densenet-tensorflow include: wang-xinyu/tensorrtx — tensorrtx is a computer vision inference engine and model implementation library designed for graphics processor… fchollet/deep-learning-models — This project is a collection of deep learning tools for image classification and audio tagging, providing a repository… bamos/densenet.pytorch — A PyTorch implementation of DenseNet. bruinxiong/modified-crunet-and-residual-attention-network.mxnet — :fire::fire:A MXNet implementation of Modified CRUNet & Residual Attention Network:fire::fire:. bruinxiong/senet.mxnet — :fire::fire: A MXNet implementation of Squeeze-and-Excitation Networks (SE-ResNext, SE-Resnet, SE-Inception-v4 and… bowenbaker/metaqnn — MetaQNN Codebase.
tensorrtx is a computer vision inference engine and model implementation library designed for graphics processor acceleration. It provides a framework for optimizing deep learning models through a GPU inference optimizer, a deep learning model converter for transforming weights from frameworks like TensorFlow and PyTorch, and a custom plugin library to implement operations not natively supported by the TensorRT API. The project distinguishes itself through a comprehensive collection of pre-defined network implementations, ranging from various YOLO versions and DETR transformers for object det
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
