# Image Classification Optimization

A fine-tuned dog breed image classification model based on microsoft/resnet-152, trained on the Stanford Dogs Dataset with an accuracy of 84.08%.

Hiera is an efficient hierarchical vision Transformer model that excels in image and video tasks with fast runtime

Hiera is a hierarchical vision Transformer model that is fast, powerful, and concise, surpassing existing technologies in image and video tasks while being faster.

Hiera is a hierarchical vision Transformer model that is fast, powerful, and concise, surpassing state-of-the-art performance in a wide range of image and video tasks while significantly improving runtime speed.

Hiera is a hierarchical vision Transformer model that is fast, powerful, and concise, excelling in image and video tasks.

Hiera is a hierarchical vision Transformer model that combines speed, powerful functionality, and minimalist design, significantly surpassing existing technical standards in image and video tasks with outstanding computational efficiency.

Hiera is a hierarchical vision Transformer model that is fast, powerful, and extremely concise. It surpasses current state-of-the-art techniques in a wide range of image and video tasks while achieving significant speed improvements.

A facial expression recognition model fine-tuned based on the ResNet-50 architecture, trained on the FER2013 dataset with an accuracy of 68.47%

YOLOv5n-cls-v7.0 is a lightweight image classification model based on the YOLOv5 architecture, suitable for efficient visual task processing.

RegNet model trained on ImageNet-1k, an efficient vision model designed through neural architecture search

RegNet model trained on ImageNet-1k, an efficient vision model designed via neural architecture search

ResNet26 is an image classification model based on the deep residual learning architecture, a variant in the ResNet series.

RepVGG-A0 is an efficient convolutional neural network based on the VGG-style architecture, achieving separation between training and inference structures through structural reparameterization techniques to enhance performance.

ResNet152 is an image classification model based on deep residual learning, which solves the gradient vanishing problem in deep network training through residual connections.

ResNet26d is an image classification model based on deep residual learning, a variant (d) version of ResNet, optimized for stem structure and shortcut connections.

ResNet18 is an image classification model based on deep residual learning, which solves the difficulty of training deep networks through residual connections.

ResNet50 is an image classification model based on deep residual learning, which solves the vanishing gradient problem in deep neural networks through residual connections.