Mariosolerzhawhugging/fruit-vit-model

Model Card for Fruit Classification ViT Model

Model Details

Model Description

This model is a Vision Transformer (ViT) fine-tuned for fruit image classification. It classifies images into six categories:

• Banana
• Mango
• Orange
• Pitaya
• Pomegranate
• Tomatoes

The model is based on transfer learning using a pretrained ViT architecture and has been fine-tuned on a subset of the Fruit Recognition dataset.

• Developed by: Mario Soler Vidal
• Model type: Vision Transformer (ViT) for image classification
• Language(s): Not applicable (Computer Vision)
• License: MIT (or leave blank if unsure)
• Finetuned from model: google/vit-base-patch16-224

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Model Sources

• Repository: https://huggingface.co/Mariosolerzhawhugging/fruit-vit-model
• Demo: https://huggingface.co/spaces/Mariosolerzhawhugging/fruit-classification-app

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Uses

Direct Use

This model can be used to classify images of fruits into one of the six supported categories. It is suitable for:

• Educational demonstrations
• Image classification tasks
• Computer vision experiments

Downstream Use

The model can be integrated into applications such as:

• Web apps (e.g., Gradio, Streamlit)
• Retail or inventory systems
• Automated fruit recognition pipelines

Out-of-Scope Use

• Classification of fruits outside the six trained classes
• Complex real-world environments with heavy occlusion
• Non-fruit image classification

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Bias, Risks, and Limitations

The model was trained on images with mostly clean and controlled backgrounds, which may introduce bias.

Potential limitations:

• Reduced performance in highly cluttered or noisy environments
• Limited generalization to unseen fruit types
• Sensitivity to extreme lighting or image distortions

Recommendations

• Use the model primarily for the six trained fruit classes
• Validate performance in real-world scenarios before deployment
• Consider further fine-tuning for more diverse datasets

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Training Details

Training Data

The model was trained on a subset of the Fruit Recognition dataset, which contains over 44,000 images.

Key characteristics:

• Images captured in a controlled lab environment
• Resolution: 320 × 258 pixels
• Mostly clean backgrounds
• Variations in lighting, shadows, and pose

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Training Procedure

Preprocessing

• Images resized to 224 × 224 pixels
• Normalized using ImageNet statistics
• Converted to RGB format

Data augmentation:

• Random horizontal flip
• Rotation
• Color jitter

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Training Hyperparameters

• Training regime: fp32
• Approach: Transfer learning (frozen backbone, trained classification head)

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Evaluation

Testing Data, Factors & Metrics

Testing Data

Validation and test splits from the Fruit Recognition dataset.

Metrics

• Accuracy
• Macro F1 Score

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Results

• Accuracy: ~0.999
• Macro F1 Score: ~0.999

Summary

The model achieves very high performance with confident predictions across all classes.

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Technical Specifications

Model Architecture and Objective

• Vision Transformer (ViT)
• Image classification objective

Compute Infrastructure

Hardware

GPU (training environment)

Software

• Python
• Hugging Face Transformers
• PyTorch

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Model Card Authors

Mario Soler Vidal