svnm 1.4.1

A package to make the usage of DeepLearning models easier

svnm - A Simplified Deep Learning Models Package

svnm is a Python package designed to simplify the process of using deep learning models in your code. With just a simple import, you can easily load pre-trained models and make predictions with minimal setup. This package is aimed at making deep learning models more accessible for developers, enabling rapid experimentation and deployment.

Routing

Image Classification

• Gender Detection
• Face Detection
• Face Recognition
• Handwritten Digit Recognition

Features

• Easy-to-use interface: Import the package and start making predictions with minimal code.
• Pre-trained models: Includes popular deep learning models ready for use without additional training.
• Flexible prediction methods: Support for various types of input data to make predictions.
• Lightweight and efficient: Focuses on simplifying deep learning workflows without overhead.

Installation

To install svnm, simply use pip:

pip install svnm

Model Usage

Gender Detection

from svnm.models import GenderDetection

model = GenderDetection()

# Single Prediction
prediction, confidence = model.predict("face_image.jpg")
print(f"Predicted Gender: {prediction} with confidence {confidence:.2f}")

# Batch Prediction
image_paths = ["face1.jpg", "face2.jpg", "face3.jpg"]
predictions = model.predict_batch(image_paths)
for img, (label, conf) in zip(image_paths, predictions):
    print(f"Image: {img} -> Predicted Gender: {label} with confidence {conf:.2f}")

# Visualization
model.visualize_prediction("face_image.jpg")

How Gender Detection Works

The Gender Detection model in svnm classifies whether a detected face belongs to a male or female. It works as follows:

1. Loading the Model: The pre-trained deep learning model is loaded automatically when the GenderDetection class is initialized.
2. Processing the Image: The input image is preprocessed and resized before being fed into the model.
3. Making Predictions: The model analyzes the facial features and predicts the gender along with a confidence score.
4. Batch Processing:
   • Supports batch predictions for multiple images at once.
   • Each image is processed individually, and the results are returned as a list.
5. Visualization:
   • If visualize_prediction() is called, the image is displayed with the predicted gender and confidence score.

Face Detection

from svnm.models import FaceDetection

model = FaceDetection(save=True, save_dir="predictions")

# Single Image Prediction
predictions = model.predict("face_image.jpg")
print("Face Detection Results:", predictions)

# Batch Prediction
image_paths = ["face1.jpg", "face2.jpg", "face3.jpg"]
batch_predictions = model.predict_batch(image_paths)
for img, preds in zip(image_paths, batch_predictions):
    print(f"Image: {img} -> Predictions: {preds}")

How Face Detection Works

The Face Detection model in svnm is designed to detect human faces in images. It works as follows:

1. Loading the Model: The pre-trained YOLO-based face detection model is loaded automatically when the FaceDetection class is initialized.
2. Processing the Image: The input image is read, preprocessed, and fed into the model.
3. Detecting Faces: The model detects faces in the image and returns bounding box coordinates, confidence scores, and class labels.
4. Batch Processing:
   • Supports batch predictions for multiple images at once.
   • Each image is processed individually, and the results are returned as a list.
5. Saving and Visualizing Results:
   • If save=True, the detected faces are highlighted in the image and saved to the specified directory.
   • The model outputs a list of detected faces with their positions and confidence levels.

Face Recognition

from svnm.models import FaceRecognition

model = FaceRecognition()

# Train the model
train_dataset = [
    {"imagepath": "person1.jpg", "label": "John"},
    {"imagepath": "person2.jpg", "label": "Jane"}
]
model.train(train_dataset)

# Predict from a new image
prediction = model.predict("test_face.jpg")
print("Face Recognition Prediction:", prediction)

How Face Recognition Works

1. Face Detection: Detects faces in the given image using the FaceDetection model.
2. Face Embeddings: Extracts feature embeddings using a deep learning model.
3. Matching Faces: Compares embeddings with the trained dataset using cosine similarity.
4. Making Predictions:
   • If a match is found, returns the predicted label and confidence score.
   • If no match is found, returns Unknown.

Handwritten Digit Recognition

from svnm.models import HandwrittenDigitRecognition

model = HandwrittenDigitRecognition()

# Single Image Prediction
prediction = model.predict("digit_image.jpg")
print("Predicted Digit:", prediction)

How Handwritten Digit Recognition Works

1. Loading the Model: The pre-trained digit recognition model is initialized.
2. Processing the Image: The input image is resized and normalized before being fed into the model.
3. Making Predictions:
   • The model predicts the digit and outputs the confidence score.
   • Supports batch predictions for multiple images at once.

Example Output

{"digit": 7, "confidence": 0.98}