Embarking on the enchanting exploration of "Butterfly Classification using Deep Learning," our project harnesses the prowess of the MobileNetV2 algorithm within the TensorFlow framework to create an intricate model. This neural network, trained on a diverse dataset of butterfly images, achieves an impressive 98% accuracy during training and maintains a robust 95% accuracy in testing. Beyond the model's success, we extend the project's accessibility by developing a Gradio-based machine learning app. This intuitive application allows users to effortlessly input butterfly images, receiving swift and accurate predictions regarding the species classification. In this convergence of technology and nature, our project not only showcases the capabilities of deep learning in image recognition but also opens avenues for broader applications in ecological research and education.
The project involves creating a Gradio app for butterfly classification. It utilizes a pre-trained butterfly classification model (model.h5) and class labels stored in a CSV file (classLabel1.csv). Users can upload images of butterflies to the app, and it provides real-time predictions for the butterfly's class label.Users can upload images of butterflies to the app, and it provides real-time predictions for the butterfly's class label. The code integrates Gradio, TensorFlow, and pandas to create an interactive and user-friendly interface for butterfly classification.
The source code combines the Gradio library, TensorFlow, and pandas to create a user-friendly interface for butterfly classification. The provided Gradio app enables users to interact with the pre-trained model and obtain predictions with ease.
Here's a step-by-step description of how the code sets up a Gradio app for butterfly classification:
Importing Libraries:
The code starts by importing the necessary libraries:
gradio: Used for creating user interfaces for machine learning models.
tensorflow: Used for loading the pre-trained model.
pandas: Used for reading class labels from a CSV file.
numpy: Used for numerical operations.
python
1import gradio as gr
2import tensorflow as tf
3from tensorflow.keras.models import load_model
4from tensorflow.keras.preprocessing import image
5from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
6import pandas as pd
7import numpy as np
Loading the Trained Model:
The code loads the pre-trained butterfly classification model (model.h5) using TensorFlow's load_model function.
The predict_butterfly function is defined to take an image as input, preprocess it, make predictions using the loaded model, and return the predicted class label.
python
1defpredict_butterfly(img):2# Preprocess the image3 img_array = image.img_to_array(img)4 img_array = np.expand_dims(img_array, axis=0)5 img_array = preprocess_input(img_array)67# Make predictions using the loaded model8 predictions = model.predict(img_array)910# Custom decoding based on my model's classes11 top_prediction_index = np.argmax(predictions)12 top_prediction = class_labels[top_prediction_index]1314return top_prediction
Creating Gradio Interface:
The gr.Interface class is used to create the Gradio interface:
fn: Specifies the prediction function.
inputs: Set to gr.Image() to accept an image as input.
outputs: Set to gr.Textbox() to display the predicted class label as text.
live: Set to True for live updates.
title: Sets the title of the Gradio interface.
description: Provides a description of the interface.
python
1iface = gr.Interface(2 fn=predict_butterfly,3 inputs=gr.Image(),4 outputs=gr.Textbox(),5 live=True,6 title='Butterfly Classification App',7 description='Upload an image of a butterfly to get the predicted class label.'8)
Launching the Gradio App:
The iface.launch() method is called to launch the Gradio app, providing a link to the app.
iface.launch()
Running the Code:
After running the code, users can click on the provided link to open the Gradio app in a new tab.
The app allows users to upload an image of a butterfly, and it provides real-time predictions for the butterfly's class label.
This the interface:
image
This description outlines how the code combines the Gradio library, TensorFlow, and pandas to create a user-friendly interface for butterfly classification. The provided Gradio app enables users to interact with the pre-trained model and obtain predictions with ease.
