ademaulana/plantClassification

MobileNetV3 Model for Plant Classification

Model Description

This model is a fine-tuned MobileNetV3Small trained to classify different types of plants. It was trained using transfer learning on a dataset obtained from Kaggle.

• Base Model: MobileNetV3Small (pretrained on ImageNet)
• Dataset: Plants Classification Dataset
• Accuracy: 88%
• Fine-Tuning: Last 20 layers of MobileNetV3Small were unfrozen for fine-tuning.

Dataset

The dataset consists of images of various plant species, divided into training and validation sets:

• Training Images: Preprocessed with data augmentation (rotation, shifting, zoom, brightness adjustment, etc.)
• Validation Images: Rescaled without augmentation

Model Training

The model was trained using TensorFlow and Keras, with categorical crossentropy loss and the Adam optimizer. The training process involved:

1. Data Augmentation using ImageDataGenerator.
2. Transfer Learning by leveraging MobileNetV3Small's pretrained weights.
3. Fine-Tuning of the last 20 layers.
4. Learning Rate Scheduling using ReduceLROnPlateau.
5. Evaluation using classification reports and a confusion matrix.
6. Exporting the Model as a .tflite file for mobile deployment.

Model Performance

• Training Accuracy: 88%
• Validation Accuracy: 88%
• Loss Function: Categorical Crossentropy
• Optimizer: Adam (learning rate = 0.0001)

Usage

To use the model for inference, load it using TensorFlow:

import tensorflow as tf
from tensorflow.keras.models import load_model

# Load the model
model = load_model("mobilenetv3_tanaman.h5")

# Preprocess an input image
import numpy as np
from tensorflow.keras.preprocessing import image

img_path = "path_to_image.jpg"
img = image.load_img(img_path, target_size=(224, 224))
img_array = image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)

# Make a prediction
predictions = model.predict(img_array)
class_idx = np.argmax(predictions)
print(f"Predicted class: {class_idx}")

Deployment

This model can be deployed for:

• Mobile applications (converted to .tflite for TensorFlow Lite compatibility)
• Web-based applications
• Embedded AI systems for plant classification

License

This model is provided for research and educational purposes. Please ensure to cite the original dataset from Kaggle if used in any publication.

Citation

If you use this model, please cite:

@misc{PlantClassification2024,
  title={MobileNetV3 Model for Plant Classification},
  author={Ade Maulana},
  year={2024},
  url={https://huggingface.co/your-huggingface-repo}
}