food-classifier-efficientnet

Model Card for GlycoCare+ Food Classifier (EfficientNet)

This model classifies 61 Pakistani dishes from images and serves as the food recognition component of GlycoCare+, an AI-powered nutrition and glucose insight app for diabetes, BP, and heart health management.

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Model Details

Model Description

This model uses EfficientNet-B0 architecture fine-tuned on a dataset of 61 Pakistani dishes (approx. 50 images per class).
It detects the type of food in an image and outputs a class label used to estimate nutrition, glucose impact, and personalized health advice.

• Developed by: Maheen Touqeer
• Model type: Image Classification
• Language(s): N/A (Computer Vision Model)
• License: Apache 2.0
• Finetuned from: google/efficientnet-b0

Model Sources

• Repository: https://huggingface.co/Maheentouqeer1/food-classifier-efficientnet
• Dataset: Custom 61 Pakistani Dishes Dataset (Kaggle Source: “Indian Food Images Dataset”)

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Uses

Direct Use

Used to identify food items from images to power nutrition and glucose prediction systems for GlycoCare+.

Downstream Use

Integrated into:

• Health and diet assistant apps
• Glucose prediction and nutrition insight modules
• Personalized meal recommendation systems

Out-of-Scope Use

Not designed for:

• Non-Asian or international food recognition
• Medical diagnosis or prescription generation
• Non-nutritional image recognition tasks

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Bias, Risks, and Limitations

• The dataset is limited to Pakistani/Indian cuisine; performance may degrade for other cuisines.
• Lighting, image quality, and portion occlusion can reduce accuracy.
• Model does not account for cooking variations (e.g., oil quantity, recipe changes).

Recommendations

• Use high-quality images with clear view of food.
• Fine-tune further for additional cuisines or real-world photos.

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How to Get Started with the Model

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

processor = AutoImageProcessor.from_pretrained("Maheentouqeer1/food-classifier-efficientnet")
model = AutoModelForImageClassification.from_pretrained("Maheentouqeer1/food-classifier-efficientnet")

image = Image.open("biryani.jpg")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
pred = outputs.logits.softmax(1)
label = pred.argmax(-1).item()
print(model.config.id2label[label])
Training Details
Training Data

Dataset: 61 Pakistani dishes (50 images/class)

Source: Kaggle Indian Food Images Dataset

Total Images: 3050 (Train 2440 | Validation 610)

Augmentation: RandomRotation, HorizontalFlip, Normalization

Training Procedure

Framework: PyTorch + Hugging Face Transformers

Precision: FP32

Epochs: 5

Optimizer: AdamW (lr=5e-5)

Loss: CrossEntropyLoss

Scheduler: StepLR

Training Hyperparameters
Parameter	Value
Batch Size	32
Learning Rate	5e-5
Epochs	5
Optimizer	AdamW
Precision	FP32
Evaluation
Testing Data

Validation split (610 images) from the same dataset.

Metrics

Accuracy

Precision

Recall

F1-score

Results
Metric	Value
Train Accuracy	95.0%
Validation Accuracy	87.5%
Best Epoch	5
Loss (val)	0.36
Summary

The model achieved strong generalization with consistent validation accuracy and no significant overfitting. Further fine-tuning (10–12 epochs, unfreezing more layers) can improve validation accuracy beyond 90%.
Environmental Impact

Hardware Type: NVIDIA Tesla T4

Training Time: ~3 minutes per epoch × 5 epochs

Compute Provider: Google Colab

Region: Asia-South1

Estimated Carbon Emitted: < 0.02 kg CO₂eq

Technical Specifications
Model Architecture

Base Model: EfficientNet-B0

Layers Fine-tuned: Last 3 convolutional blocks + classifier head

Output Dimension: 61 classes

Activation: Softmax

Compute Infrastructure

Hardware: Google Colab GPU (Tesla T4, 16GB RAM)

Software: PyTorch 2.1, Transformers 4.40, TorchVision 0.18

Citation

BibTeX:

@misc{maheentouqeer2025foodclassifier,
  title={GlycoCare+ Food Classifier (EfficientNet)},
  author={Touqeer, Maheen},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/Maheentouqeer1/food-classifier-efficientnet}}
}