# Image classification

A lightweight image classification model generated by HuggingPics for gender classification tasks.

Open CLIP is a zero-shot image classification model capable of classifying images without fine-tuning.

An image classification model fine-tuned based on Google's ViT-base-patch16-224 model, achieving an accuracy of 68.97% on the evaluation set

MobileNet V2 is a lightweight convolutional neural network designed for mobile devices, achieving a balance between latency, model size, and accuracy.

This model is a face recognition model fine-tuned on an image folder dataset based on Google's ViT architecture, achieving near-perfect accuracy on the evaluation set.

A small vision Transformer model trained using the DINOv2 method, suitable for image feature extraction and classification tasks

A vision Transformer model fine-tuned based on google/vit-base-patch16-224, suitable for image classification tasks

A vision classification model fine-tuned on the Diatome dataset based on Google's ViT model, achieving 94.29% accuracy

A vision model fine-tuned based on google/vit-base-patch16-224, specific purpose not clearly stated

A facial expression recognition model fine-tuned based on Google's ViT model, trained on the FER2013 dataset with an accuracy of 70.22%.

This is an image classification model based on the BEiT architecture, fine-tuned on the FER2013 and CK+ datasets for facial expression recognition tasks.

A vision Transformer model fine-tuned based on Google's ViT foundation model, suitable for image classification tasks

An image classification model built with PyTorch and HuggingPics for recognizing gender in images

An animal image classification model based on PyTorch and HuggingPics, capable of recognizing multiple animal categories.

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

An image classification model built on PyTorch and HuggingPics, specifically designed for food classification

Vision Transformer model fine-tuned from google/vit-base-patch16-224, suitable for image classification tasks

A Vision Transformer model pre-trained using the MSN method, suitable for few-shot image classification tasks

This model is a fine-tuned Vision Transformer based on google/vit-base-patch16-224-in21k on an unknown dataset, primarily used for image classification tasks.

This is an INT8 PyTorch model statically quantized using Intel® Neural Compressor post-training, based on Google's ViT model fine-tuning, significantly reducing model size while maintaining high accuracy.

An image classification model based on PyTorch and HuggingPics, capable of accurately identifying multiple animal categories.

Data2Vec-Vision is a self-supervised learning model based on the BEiT architecture, fine-tuned on the ImageNet-1k dataset, suitable for image classification tasks.

Data2Vec-Vision is a self-supervised learning vision model based on the BEiT architecture, fine-tuned on the ImageNet-1k dataset, suitable for image classification tasks.

Data2Vec-Vision is a self-supervised learning model based on the BEiT architecture, pretrained on the ImageNet-1k dataset, suitable for image classification tasks.

RegNet model trained on imagenet-1k, pre-trained in a self-supervised manner on billions of random web images before fine-tuning

Image classification model based on Vision Transformer architecture, pre-trained on ImageNet-21k dataset and fine-tuned with MIT indoor scene dataset