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Small window attention

# Small window attention

Swin Tiny Patch4 Window7 224 Cifar10

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

Image Classification

Swin Tiny Patch4 Window7 224 Finetuned Main Gpu 20e Final

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

Image Classification

Swinv2 Tiny Patch4 Window8 256 Finetuned Og Dataset 10e Finetuned Og Dataset 10e

A lightweight image classification model based on SwinV2 architecture, fine-tuned on an image folder dataset with 97.83% accuracy

Image Classification

Swin Tiny Patch4 Window7 224 Finetuned Og Dataset 10e Finetuned Og Dataset 10e

This is a vision model based on the Swin Transformer architecture, which performs excellently in image classification tasks after fine-tuning.

Image Classification

Swinv2 Tiny Patch4 Window8 256 Finetuned Og Dataset 10e

Fine-tuned vision model based on Swin Transformer V2 architecture, excelling in image classification tasks

Image Classification

Swinv2 Tiny Patch4 Window8 256 Finetuned Og Dataset 5e

This model is a fine-tuned image classification model based on the Swin Transformer V2 Tiny architecture, achieving 96.35% accuracy on the evaluation set.

Image Classification

Swin Tiny Patch4 Window7 224 Finetuned Eurosat

Fine-tuned image classification model based on Swin Transformer Tiny architecture, trained on image folder dataset

Image Classification

Swin Tiny Patch4 Window7 224 Finetuned Eurosat

This model is a fine-tuned version based on the Swin Transformer architecture, specifically designed for image classification tasks, achieving 97.26% accuracy on the EuroSAT dataset.

Image Classification

Annabelleabbott

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