zeyuai/efficientnet-food-classifier

EfficientNet Food Classifier

A fine-tuned EfficientNet-B0 model for classifying food images into 8 categories. Trained using a two-stage transfer learning approach with ImageNet pre-trained weights.

Model Description

• Architecture: EfficientNet-B0 + custom classification head
• Task: Image Classification (8 food categories)
• Framework: TensorFlow / Keras
• Input: 224×224 RGB images
• Pre-training: ImageNet

Classes

ID	Label
0	Baked Potato
1	Burger
2	Crispy Chicken
3	Donut
4	Fries
5	Hot Dog
6	Pizza
7	Sandwich

Training

Two-Stage Transfer Learning

1. Stage 1 — Feature extraction: Backbone frozen, only classification head trained (LR: 1e-3)
2. Stage 2 — Fine-tuning: Backbone unfrozen (BatchNorm frozen), full model trained (LR: 2e-5)

Training Configuration

Parameter	Value
Base Model	EfficientNet-B0 (ImageNet)
Image Size	224 × 224
Batch Size	8
Stage 1 Epochs	12 (EarlyStopping patience=3)
Stage 2 Epochs	6
Optimizer	Adam
Loss	Sparse Categorical Crossentropy
Dropout	0.2
Data Augmentation	RandomFlip, RandomRotation(0.05), RandomZoom(0.1), RandomContrast(0.1)

Dataset

Split	Images
Train	3,797
Validation	813
Test	814
Total	5,424

• Stratified 70/15/15 split with leak-free guarantee (SHA256 dedup)
• Images sourced via DuckDuckGo image search, manually cleaned

Evaluation

Overall Metrics (Test Set)

Metric	Score
Accuracy	0.9328
Precision (weighted)	0.9345
Recall (weighted)	0.9328
F1 (weighted)	0.9331
F1 (macro)	0.9301

Per-Class Performance

Class	Precision	Recall	F1-score	Support
Baked Potato	0.907	0.898	0.903	98
Burger	0.934	0.904	0.919	94
Crispy Chicken	0.860	0.968	0.911	95
Donut	0.986	0.958	0.971	142
Fries	0.926	0.917	0.921	96
Hot Dog	0.957	0.957	0.957	94
Pizza	0.971	0.918	0.944	73
Sandwich	0.906	0.923	0.914	52

Available Formats

Format	File	Size	Use Case
Keras	BestModelEfficientNetLite.keras	16 MB	Python / TensorFlow
TFLite	tflite/model.tflite	4.4 MB	Mobile / Edge (dynamic range quantized)
TFLite float16	tflite/model_float16.tflite	7.8 MB	Mobile / Edge (float16 quantized)
TFJS	tfjs/model.json	15 MB	Browser / Node.js

Usage

Python (Keras)

import tensorflow as tf
import numpy as np
from PIL import Image

# Load model
model = tf.keras.models.load_model(
    "BestModelEfficientNetLite.keras",
    custom_objects={"preprocess_input": tf.keras.applications.efficientnet.preprocess_input},
)

# Predict
img = Image.open("food.jpg").resize((224, 224))
x = np.expand_dims(np.array(img), axis=0).astype("float32")
probs = model.predict(x)[0]

classes = ["Baked Potato", "Burger", "Crispy Chicken", "Donut", "Fries", "Hot Dog", "Pizza", "Sandwich"]
print(f"Predicted: {classes[np.argmax(probs)]} ({probs.max():.1%})")

TFLite (Python)

import numpy as np
from PIL import Image
import tflite_runtime.interpreter as tflite

interpreter = tflite.Interpreter(model_path="tflite/model.tflite")
interpreter.allocate_tensors()

img = np.array(Image.open("food.jpg").resize((224, 224)), dtype=np.float32)
img = np.expand_dims(img, axis=0)

interpreter.set_tensor(interpreter.get_input_details()[0]['index'], img)
interpreter.invoke()
output = interpreter.get_tensor(interpreter.get_output_details()[0]['index'])

TFJS (JavaScript)

import * as tf from '@tensorflow/tfjs';

const model = await tf.loadGraphModel('tfjs/model.json');
const img = tf.browser.fromPixels(imageElement).resizeBilinear([224, 224]).expandDims(0).toFloat();
const predictions = model.predict(img);
const classIndex = predictions.argMax(-1).dataSync()[0];

Limitations

• Trained on web-scraped images; may not generalize well to all food photography styles
• Limited to 8 food categories
• Best performance on clearly visible, single-item food images

License

Apache 2.0