Vit Base Patch16 224 In21k Snacks
A Vision Transformer model pre-trained on ImageNet-21k and fine-tuned specifically for snack image classification tasks
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Release Time : 5/14/2022
Model Overview
This model is a Vision Transformer pre-trained on ImageNet-21k and fine-tuned on the Matthijs/snacks dataset, specifically designed for snack image classification tasks.
Model Features
High Accuracy Classification
Achieves 89.29% accuracy on the snack test set
Data Augmentation
Utilizes various data augmentation techniques including random cropping, horizontal flipping, and sharpness adjustment
Transfer Learning
Fine-tuned based on the large-scale ImageNet-21k pre-trained model
Model Capabilities
Snack Image Classification
Food Recognition
Visual Feature Extraction
Use Cases
Retail & Food Service
Automatic Checkout System
Used in supermarkets to automatically identify snack products selected by customers
Can replace manual scanning, improving checkout efficiency
Food Inventory Management
Automatically identifies snack products on shelves
Helps monitor inventory in real-time
Health & Nutrition
Diet Tracking App
Automatically records snacks consumed by users through photos
Helps users track their eating habits
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