zutaars1/vit-computer-vision-classification-model

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vit-base-mini-food-3

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the Mini Food-3 custom dataset. It achieves the following results on the evaluation set:

• —Loss: 0.5656
• —Accuracy: 0.9067

Model description

This is a Vision Transformer (ViT) model fine-tuned for food image classification. The model was trained to classify three food categories: pizza, sushi, and ice cream.

Intended uses & limitations

This model is intended for classifying images of three food types:

• —pizza
• —sushi
• —ice_cream

It performs best on images similar to the training dataset (Food-101 subset). The model may not generalize well to other food categories or different image conditions.

Training and evaluation data

The model was trained on the Mini Food-3 dataset, derived from the Food-101 dataset:

Preprocessing

• —Images converted to RGB
• —Resizing and normalization using ViT image processor
• —Automatic label encoding from folder structure

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 0.00002
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —optimizer: adamw_torch with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 3
• —weight_decay: 0.01

Training results

Framework versions

• —Transformers 4.50.0
• —PyTorch 2.0+
• —Datasets 2.0+
• —Tokenizers 0.13+

Model Comparison

This fine-tuned ViT model was compared against:

• —CLIP (Zero-Shot): openai/clip-vit-large-patch14 - No training required
• —OpenAI Vision Model: LLM-based image classification

The fine-tuned ViT model outperforms both baseline approaches on this specific dataset due to task-specific training.