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Snacks Classifier
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Snacks Classifier

Developed by Matthijs
A lightweight image classification model based on Microsoft's Swin Transformer Tiny architecture, achieving 92.86% test accuracy after fine-tuning on a snack classification dataset
Image Classification
Transformers
#Snack Classification#High-precision Recognition#Lightweight Vision Model
Downloads 15
Release Time : 4/14/2022

Model Overview

This is a vision Transformer model fine-tuned for snack classification tasks, suitable for scenarios like food recognition and retail shelf management

Model Features

Efficient Local Attention Mechanism
Utilizes sliding window attention patterns to significantly reduce computational complexity while maintaining accuracy
Lightweight Architecture
The Tiny version is particularly suitable for deployment in resource-constrained environments
Transfer Learning Optimization
Demonstrates excellent domain adaptation capabilities after fine-tuning on snack datasets

Model Capabilities

Image Classification
Food Recognition
Retail Product Recognition

Use Cases

Retail Industry
Automatic Shelf Inventory
Identify the types and locations of snack products on shelves
Test set accuracy 92.86%
Self-checkout System
Automatically recognize snack products selected by customers through cameras
Health Management
Diet Recording Assistance
Automatically identify and record the types of snacks consumed by users
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