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Swin Base Finetuned Snacks
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Swin Base Finetuned Snacks

Developed by aspis
A snack image classification model based on the Swin Transformer architecture, achieving an accuracy of 94.55% after fine-tuning on a snack dataset
Image Classification
Transformers
Open Source License:Apache-2.0#Snack Classification#High-precision Image Recognition#Swin Transformer
Downloads 15
Release Time : 6/8/2022

Model Overview

This model is an image classification model fine-tuned on a snack dataset based on microsoft/swin-base-patch4-window7-224, primarily used for identifying and classifying different types of snack images.

Model Features

High Accuracy
Achieves a classification accuracy of 94.55% on the snack dataset
Based on Swin Transformer
Utilizes the advanced Swin Transformer architecture, suitable for image classification tasks
Fine-tuned Model
Fine-tuned on the base model to optimize snack classification performance

Model Capabilities

Image Classification
Snack Recognition

Use Cases

Retail Industry
Automatic Product Classification
Used in automatic product classification systems for supermarkets or retail stores
Accurately identifies different snack categories
Inventory Management
Assists inventory management systems in automatically identifying and classifying snack products
Improves inventory management efficiency
Food Analysis
Food Ingredient Analysis
Assists food ingredient analysis systems in identifying snack categories
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