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Swin Tiny Patch4 Window7 224 Finetuned Cifar10
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Swin Tiny Patch4 Window7 224 Finetuned Cifar10

Developed by nielsr
This model is an image classification model fine-tuned on the CIFAR-10 dataset based on the Swin Transformer Tiny architecture, achieving an accuracy of 97.89%.
Image Classification
Transformers
Open Source License:Apache-2.0#High-precision image classification#Swin-Transformer architecture#Few-shot fine-tuning
Downloads 26
Release Time : 4/11/2022

Model Overview

This is a vision Transformer model based on the Swin Transformer Tiny architecture, specifically designed for image classification tasks. Fine-tuned on the CIFAR-10 dataset, the model can efficiently and accurately recognize 10 common object categories.

Model Features

High accuracy
Achieves 97.89% classification accuracy on the CIFAR-10 test set.
Efficient architecture
Lightweight architecture based on Swin Transformer, balancing computational efficiency and model performance.
Transfer learning
Adapted to specific classification tasks through fine-tuning after pre-training on large-scale datasets.

Model Capabilities

Image classification
Object recognition
Feature extraction

Use Cases

Computer vision
Object classification system
Used to build automatic classification systems for recognizing common objects.
Achieves 97.89% accuracy on the CIFAR-10 dataset.
Educational tool
Can serve as a teaching demonstration model to showcase deep learning image classification capabilities.
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