# Lightweight Image Classification

A compact ResNet18 model trained on the CIFAR-10 dataset, achieving a test accuracy of 94.98%.

A small ResNet18 image classification model trained on the CIFAR-100 dataset, achieving a test accuracy of 79.26%.

EfficientNet is a mobile-friendly pure convolutional neural network that achieves efficient scaling by uniformly adjusting depth/width/resolution dimensions through compound coefficients.

EfficientNet is a mobile-friendly pure convolutional model that uniformly scales depth/width/resolution dimensions via compound coefficients, trained on the ImageNet-1k dataset.

This is an image classification model generated based on HuggingPics, specifically designed to recognize and classify images into four categories: receipts, paper, invoices, and documents.

This model is a vision classification model fine-tuned on a sausage (klobasa) image dataset based on the ConvNeXt-Tiny architecture, achieving an accuracy of 89.58%

MobileNet V2 is a lightweight vision model optimized for mobile devices, excelling in image classification tasks.

A lightweight image classification model pre-trained on the ImageNet-1k dataset, specifically optimized for mobile devices

This model is an image classification model fine-tuned on the beans dataset based on timm/resnet18.a1_in1k, achieving an accuracy of 69.17%.

This model is a tiny version based on the ConvNeXT architecture, fine-tuned on the CIFAR10 dataset, suitable for image classification tasks.

A dog image classification model based on PyTorch and HuggingPics, capable of accurately identifying multiple rare dog breeds.

MobileNet V2 is a lightweight convolutional neural network designed for mobile devices, excelling in image classification tasks.

MobileNet V2 is a lightweight convolutional neural network designed for mobile devices, excelling in image classification tasks.

MobileNet V1 is a lightweight convolutional neural network designed for mobile and embedded vision applications, pretrained on the ImageNet-1k dataset.

A small ResNet model for MNIST handwritten digit recognition, achieving an accuracy of 0.985 on the validation set.

This is an image classification model for identifying rare dog breeds with an accuracy of 64.44%.

A kitchen-related item image classification model built based on PyTorch and HuggingPics

This is an image classification model based on the PyTorch framework, specifically designed to distinguish between African Pygmy Goats and Nigerian Dwarf Goats.