# High-precision Image Classification

The Perception Encoder (PE) is a state-of-the-art image and video understanding encoder trained through simple vision-language learning, achieving top performance across various visual tasks.

A pre-trained model based on the ResNet-50 architecture, specifically fine-tuned for Central Asian food image classification tasks, supporting 34 types of Central Asian food classification.

The AIMv2 series are vision models pretrained with multimodal autoregressive objectives, demonstrating excellent performance across multiple benchmarks.

This is an image classification model based on Vision Transformer (ViT) and BEiT architectures, specifically designed for recognizing Indonesian batik patterns.

The first hybrid computer vision model combining the advantages of Mamba and Transformer, enhancing visual feature modeling capabilities through redesigned Mamba formulation

This is an image classification model generated by HuggingPics, capable of classifying various images such as animals, birds, and flags.

This is a visual image classification model trained on the ImageNet-1k dataset, capable of recognizing various common objects and scenes.

High-precision fruit image classification model fine-tuned on the Fruits-360 dataset based on CLIP ViT-Large

A vision model for image classification, capable of recognizing various common objects and scenes.

This is a binary classification image classification model trained via AutoTrain, demonstrating perfect validation metrics.

An image classification model based on the Swin Transformer Tiny architecture, fine-tuned on the CIFAR10 dataset with an accuracy of 97.24%

This is a multi-category image classification model trained using AutoTrain, achieving an outstanding accuracy of 98.9% on the validation set.

High-precision image classification model fine-tuned on the beans dataset based on Google's ViT model

This is a multi-class image classification model trained via AutoTrain, demonstrating perfect classification performance on test datasets

This is a binary classification model trained via AutoTrain, specifically designed for image classification tasks.

This is a binary classification model fine-tuned based on the ViT architecture, designed to distinguish images of Bart Simpson and Homer Simpson.

A fine-tuned model based on Swin Transformer architecture for garbage classification tasks, achieving 88.27% accuracy

A fine-tuned image classification model based on ConvNeXt-Tiny architecture, achieving 98.15% accuracy on the EuroSAT dataset

This model is a fine-tuned image classification model based on the ConvNeXt-Tiny architecture, trained on the EuroSAT dataset with albumentations for data augmentation, achieving 98.48% accuracy on the evaluation set.

This model is an image classification model fine-tuned on the CIFAR-100 dataset based on the Swin Transformer architecture, achieving an accuracy of 92.01%.

This model is a fine-tuned version based on the ConvNeXt-Tiny architecture, specifically designed for image classification tasks, achieving an accuracy of 95.37% on the EuroSAT dataset.

An image classification model fine-tuned on the beans dataset based on Google's ViT model, used to identify the health status of bean leaves

An image classification model fine-tuned on the beans dataset based on Google's ViT base model, achieving 98.5% accuracy

Image classification model fine-tuned on image folder dataset based on Google's ViT model, achieving 95.02% accuracy

Image classification model based on Swin Transformer Tiny architecture, fine-tuned for 130 epochs on a specific image dataset

This model is a fine-tuned version of facebook/levit-192 on an unlabeled dataset, demonstrating excellent performance in precision, recall, F1 score, and accuracy.

A classification model based on Hugging Face Vision Transformer for the Animal-10 dataset, achieving an accuracy of 98.09%

A fine-tuned image classification model based on Swin Transformer architecture, achieving 95.17% accuracy on image classification tasks

This model is a fine-tuned Vision Transformer based on google/vit-base-patch16-384, primarily used for image classification tasks.

Fine-tuned model based on Google Vision Transformer (ViT) architecture, suitable for image classification tasks

An image classification model fine-tuned on image folder dataset based on Microsoft's ResNet-50 model, achieving 97.64% accuracy

This model is an image classification model fine-tuned on the sudo-s/herbier_mesuem7 dataset based on google/vit-base-patch16-224-in21k, achieving an evaluation accuracy of 97.31%.

This is an image classification model built with PyTorch and HuggingPics, specifically designed for detecting gum health conditions.

A fine-tuned model based on the ConvNeXt-Tiny architecture, optimized for image classification tasks, excelling on the EuroSAT dataset

A dog food image classification model fine-tuned based on Swin Transformer Tiny architecture, achieving 98.8% accuracy on the test set

This is an image classification model based on the Vision Transformer architecture, achieving an accuracy of 95.02%.

This is a vision model based on the Swin Transformer Tiny architecture, fine-tuned on the EuroSAT dataset, primarily used for image classification tasks.

This is a tiny model based on the Swin Transformer architecture, specifically designed for image classification tasks and fine-tuned on the EuroSAT dataset.

ConvNeXT is a pure convolutional model inspired by vision Transformers, trained on the ImageNet-1k dataset at 224x224 resolution.

This is a vision Transformer model based on Google's ViT base model, fine-tuned on the CIFAR10 dataset for image classification tasks.