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https://github.com/bniladridas/imageclassification
Image Classification with InceptionV3
https://github.com/bniladridas/imageclassification
computer-vision data-science deep-learning machine-learning python
Last synced: about 1 month ago
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Image Classification with InceptionV3
- Host: GitHub
- URL: https://github.com/bniladridas/imageclassification
- Owner: bniladridas
- License: mit
- Created: 2024-01-28T23:02:22.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-09-09T07:19:12.000Z (4 months ago)
- Last Synced: 2024-10-20T05:40:52.658Z (2 months ago)
- Topics: computer-vision, data-science, deep-learning, machine-learning, python
- Language: Python
- Homepage:
- Size: 257 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# π Image Classification with InceptionV3
_A Deep Learning Masterpiece_
---
## π οΈ **Tech Stack**
 Β  Β  Β 
---
## π **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!
---
## π **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.
---
## πΌοΈ **Example Results**
Hereβs a sneak peek of what the **InceptionV3** model can do!
**Predictions**:
- π **Freight Car** (Confidence: 85%)
- β‘ **Electric Locomotive** (Confidence: 8%)
- π **Passenger Car** (Confidence: 1%)
---
## βοΈ **Installation**
Make sure to install the necessary dependencies:
```bash
pip install tensorflow keras numpy
```
---
## οΏ½ββοΈ **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:
```python
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.
---
## οΏ½π **Acknowledgments**
Special thanks to:
- **TensorFlow Keras Applications** for providing the InceptionV3 model.
- **ImageNet** for the class indices: [Download](https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json).
---
## π **License**
Licensed under the [MIT License](https://github.com/niladrridas/image-classification/blob/main/LICENSE).
---
## π **Getting Started**
Start classifying images in three simple steps:
1. Clone this repository:
```bash
git clone https://github.com/niladrridas/imageclassification.git
```
---
Now, youβre all set to dive into **image classification** and harness the power of deep learning! π