# High-precision image classification

The first hybrid computer vision model combining the strengths of Mamba and Transformer, enhancing visual feature modeling efficiency by reconstructing the Mamba formula, and introducing self-attention modules in the final layers of the Mamba architecture to improve long-range spatial dependency modeling.

An image classification model based on Google Vision Transformer (ViT) architecture, specifically fine-tuned for the beans dataset

This is an auto-trained image classification model capable of recognizing multiple common object categories

A cat-dog image classification model fine-tuned based on microsoft/resnet-50, achieving 95% accuracy on the evaluation set

An image classification model fine-tuned based on the ResNet-50 architecture, specifically designed to distinguish between images of cats and dogs with an accuracy of 96.88%.

A ResNet-50 fine-tuned tomato leaf disease image classification model with 99.56% accuracy

A fine-tuned tomato leaf disease classification model based on Google Vision Transformer (ViT) architecture, achieving 99.67% accuracy on the evaluation set

This is an AI image classification model trained with AutoTrain, capable of accurately identifying and classifying various image contents.

This model is a fine-tuned image classification model based on Google's ViT-base-patch16-224 on an image folder dataset, achieving 89.21% accuracy on the validation set.

An image classification model fine-tuned based on Google Vision Transformer (ViT) architecture, used to distinguish AI-generated images from real images

An image classification model fine-tuned on the cats_vs_dogs dataset using Google's ViT model, designed to distinguish between images of cats and dogs.

A vision model fine-tuned based on google/vit-base-patch16-224, achieving an F1 score of 0.9931 on the evaluation set

This model is a fine-tuned image classification model based on google/vit-base-patch16-224-in21k on the CIFAR10 dataset, achieving an accuracy of 96.78%.

An image classification model fine-tuned on the CIFAR-10 dataset based on the Vision Transformer (ViT) architecture, achieving 99.2% accuracy

An image classification model based on Google's Vision Transformer architecture, fine-tuned specifically for content moderation tasks, achieving 90.43% accuracy on the test set

This is a model for image classification tasks, supporting the English language and adopting an open license.

A tiny model based on Swin Transformer architecture, specifically fine-tuned for CIFAR-10 image classification tasks

An image classification model fine-tuned from google/vit-base-patch16-224-in21k, achieving 97.74% accuracy on the bean dataset

Lightweight image classification model based on DeiT-tiny architecture, achieving 98.56% validation accuracy after fine-tuning on a custom image dataset

A binary classification image classification model trained with AutoTrain, performing excellently on the validation set

A computer vision model based on ConvNeXt-Tiny architecture, fine-tuned on specific datasets for image classification tasks

Fine-tuned version based on Google's ViT model, achieving 99.4% validation accuracy on image classification tasks

A fine-tuned image classification model based on the Swin Transformer architecture, achieving 99.17% validation accuracy on the image folder dataset

This is a multi-category image classification model trained using AutoTrain, excelling on bicycle-related datasets.

A Vision Transformer model fine-tuned from google/vit-base-patch16-224-in21k, achieving 99.26% accuracy on the evaluation set

This is a multi-class image classification model trained via AutoTrain, demonstrating outstanding performance on the validation set with all evaluation metrics reaching 1.0.

This is a binary classification visual model built through an automated training process, excelling in medical image classification tasks.

An image classification model based on Swin Transformer architecture, fine-tuned on an image folder dataset with an accuracy of 96.67%

A tomato disease image classification model based on Vision Transformer architecture, achieving 99.18% accuracy on the evaluation set

A tomato disease image classification model based on Google Vision Transformer (ViT) architecture with 98.87% accuracy

Plant seedling classification model based on BEiT architecture, fine-tuned on a balanced dataset with 96% test accuracy

This model is a fine-tuned image classification model based on microsoft/beit-base-patch16-224-pt22k-ft22k, achieving an accuracy of 93.59% on the evaluation set.

Pre-trained model based on BEiT architecture, fine-tuned on the cats_vs_dogs dataset for image classification tasks

This is a binary classification model trained via AutoTrain, specifically designed to differentiate between animal and human images.

HKDB_SDFT_Balanced_Version is a high-performance image classification model with high accuracy.

HKDB_SDFT_Unbalanced is a model for image classification tasks with high accuracy.

A multi-category image classification model trained using AutoTrain, demonstrating outstanding performance on test datasets

This is a multi-category image classification model trained via AutoTrain, demonstrating outstanding performance on the validation set with an accuracy of 97.4%.

This is a multi-category image classification model trained via AutoTrain, demonstrating outstanding performance on the validation set with an accuracy of 90.9%.

This is a multi-category image classification model trained using AutoTrain, achieving an outstanding accuracy of 98.1% on the validation set.