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CNN architecture exploration using Genetic Algorithm
The main features of aqibsaeed/genetic-cnn are: Evolutionary Algorithms, Image Classification Architectures.
Projects with overlapping indexed features include: rhiever/data-analysis-and-machine-learning-projects — This is a collection of machine learning projects, data visualization portfolios, and predictive analytics tools. The… 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/senet.mxnet — :fire::fire: A MXNet implementation of Squeeze-and-Excitation Networks (SE-ResNext, SE-Resnet, SE-Inception-v4 and… bruinxiong/modified-crunet-and-residual-attention-network.mxnet — :fire::fire:A MXNet implementation of Modified CRUNet & Residual Attention Network:fire::fire:.
This is a collection of machine learning projects, data visualization portfolios, and predictive analytics tools. The repository provides implementation examples for training predictive models, executing data analysis pipelines, and estimating metadata values through historical statistical tables. The project emphasizes evolutionary computing, utilizing genetic algorithms and programming to solve optimization problems. This includes calculating the shortest distance between geographic coordinates and automating the selection of models and hyperparameters within machine learning pipelines. Ad
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
