🥗 BEiT Fine-Tuned on Food Image Classification

(Food101 + UECFood256 + FruitVeg81)

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🚀 Model Summary

This model is a fine-tuned BEiT image encoder for food image classification, trained on a merged dataset combining three major datasets:

• Food101
• UECFood256
• FruitVeg81

It predicts 409 food classes, including fruits, vegetables, pastries, soups, desert etc.

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1. Model Details

🧠 Model Description

• Model type: Image classification (BEiT backbone)
• Base model: microsoft/beit-base-patch16-384
• Fine-tuned by: Jean Petit BIKIM (TSOTSA Team)
• Task: Food Image Classification
• Framework: 🤗 Transformers + PyTorch
• License: MIT (or specify one)
• Language: Vision-only

🔗 Model Sources

• Repository: (add link)
• Paper (optional): _In preparation _
• Demo (optional): Add Gradio link here

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2. Uses

✔️ Intended Use

• Food image recognition
• Nutrition information analysis
• Food engineering & dietetics
• Dataset enrichment
• Preprocessing for multimodal VLMs (Qwen2.5-VL, CLIP-based models)

❌ Out-of-Scope

• Medical nutrition diagnosis
• Real-time embedded recognition
• Images far outside food domain

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3. Bias, Risks & Limitations

⚠️ Limitations

• Confusion between visually similar meals

⚠️ Risks

• Misclassification may propagate errors in downstream nutrition estimation.

✔️ Recommendations

Use alongside:

• segmentation
• object detection
• multimodal reasoning models

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4. How to Use the Model

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch

processor = AutoImageProcessor.from_pretrained("your-username/your-model")
model = AutoModelForImageClassification.from_pretrained("your-username/your-model")

img = Image.open("example.jpg")
inputs = processor(img, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    pred = outputs.logits.argmax(-1).item()

print("Predicted class:", model.config.id2label[pred])