🌟 Image Classification with InceptionV3

A Deep Learning Masterpiece

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🛠️ Tech Stack

     

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🌍 Overview

Welcome to the world of cutting-edge image classification powered by InceptionV3. This project leverages a pre-trained model from ImageNet to classify images with incredible accuracy, capable of distinguishing a wide variety of objects in no time. Your go-to tool for exploring deep learning concepts!

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🚀 Features

• InceptionV3 Architecture: State-of-the-art model built for high-performance image classification.
• Pre-trained on ImageNet: Get immediate results with optimized weights from one of the largest datasets.
• Plug-and-Play Python Script: Simply test your own images effortlessly.
• Perfect for Learning: Ideal for gaining hands-on experience with deep learning and advanced image classification techniques.

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🖼️ Example Results

Here’s a sneak peek of what the InceptionV3 model can do!

Predictions:

• 🚂 Freight Car (Confidence: 85%)
• ⚡ Electric Locomotive (Confidence: 8%)
• 🚋 Passenger Car (Confidence: 1%)

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⚙️ Installation

Make sure to install the necessary dependencies:

pip install tensorflow keras numpy

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�‍♂️ Usage

To classify an image using the recognize_object function, follow these steps:

1. Ensure you have the required dependencies installed.
2. Use the following Python script to classify your image:

import tensorflow as tf
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.inception_v3 import preprocess_input, decode_predictions
import numpy as np

# Load pre-trained InceptionV3 model with weights trained on ImageNet
model = InceptionV3(weights='imagenet')

# Function for object recognition
def recognize_object(image_path):
    # Load and preprocess the image
    img = image.load_img(image_path, target_size=(299, 299))  # Load image and resize to 299x299 pixels
    img_array = image.img_to_array(img)  # Convert image to numpy array
    img_array = np.expand_dims(img_array, axis=0)  # Expand dimensions to match the model's input shape
    img_array = preprocess_input(img_array)  # Preprocess the image array for the InceptionV3 model

    # Make predictions
    predictions = model.predict(img_array)  # Predict the probabilities for each class

    # Decode predictions
    decoded_predictions = decode_predictions(predictions, top=3)[0]  # Decode the top 3 predictions

    # Display the top predictions
    print("Predictions:")
    for i, (imagenet_id, label, score) in enumerate(decoded_predictions):  # Iterate over the top predictions
        print(f"{i + 1}: {label} ({score:.2f})")  # Print the label and score for each prediction

# Example usage
image_path = '/path/to/your/image.jpg'  # Path to the image file
recognize_object(image_path)  # Call the function to recognize objects in the image

Replace /path/to/your/image.jpg with the path to your image file.

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�🎓 Acknowledgments

Special thanks to:

• TensorFlow Keras Applications for providing the InceptionV3 model.
• ImageNet for the class indices: Download.

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📜 License

Licensed under the MIT License.

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🏁 Getting Started

Start classifying images in three simple steps:

1. Clone this repository:

   git clone https://github.com/niladrridas/imageclassification.git

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Now, you’re all set to dive into image classification and harness the power of deep learning! 🌐