mobilenetv3-medicinal-laef-classification

🌿 MobileNetV3 — Medicinal Leaf Classification

A fine-tuned MobileNetV3Large model that classifies images of medicinal plant leaves into 3 categories: Aloe Vera, Neem, and Tulsi.

Model Details

Classes

⚠️ Important: Class order is alphabetical — ['Aloe Vera', 'Neem', 'Tulsi']. Use this exact order when decoding predictions.

Performance (920 test images)

Usage

import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing import image
# Load model
model = tf.keras.models.load_model("last_40_layers_dense_batchnorm.keras")
class_names = ['Aloe Vera', 'Neem', 'Tulsi']  # alphabetical order
def predict(img_path):
    img = image.load_img(img_path, target_size=(224, 224))
    img_array = image.img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0)  # shape: (1, 224, 224, 3)
    pred = model.predict(img_array)[0]
    pred_class = class_names[np.argmax(pred)]
    confidence = float(np.max(pred))
    return pred_class, confidence
label, conf = predict("your_leaf_image.jpg")
print(f"Prediction: {label} ({conf:.2%} confidence)")

Training Details

• Fine-tuning strategy: Last 40 layers of MobileNetV3Large unfrozen
• Head: GlobalAveragePooling2D → Dropout(0.2) → Dense(3, softmax) + BatchNorm variant
• Phase 1: Head-only training — 10 epochs, lr=0.001
• Phase 2: Fine-tuning — 15 epochs, lr=1e-5
• Loss: Categorical Cross-Entropy
• Batch size: 32
• Train/Val split: 80/20 (5904 train / 1476 val images)