Gender Classification Quantized Model

This repository hosts a quantized version of a feedforward neural network model, fine-tuned for gender classification tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.

Model Details

Model Name: Gender Classifier
Model Architecture: 2-layer MLP (Multi-Layer Perceptron)
Task: Gender Classification
Dataset: Gender Classification Dataset v7
Quantization: QInt8 (Dynamic Quantization)
Framework: PyTorch

Usage

Installation

pip install torch pandas scikit-learn numpy

Loading the Quantized Model

import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler, LabelEncoder
import json

# Define the model architecture
class GenderClassifier(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc = nn.Sequential(
            nn.Linear(7, 32),
            nn.ReLU(),
            nn.Linear(32, 2)
        )

    def forward(self, x):
        return self.fc(x)

# Load the quantized model
model = GenderClassifier()
quantized_model = torch.quantization.quantize_dynamic(model, {nn.Linear}, dtype=torch.qint8)
quantized_model.load_state_dict(torch.load("quantized_model/pytorch_model.bin"))

# Load configuration
with open("quantized_model/config.json", "r") as f:
    config = json.load(f)

# Example usage
# Prepare your input data (7 features)
input_data = np.array([[feature1, feature2, feature3, feature4, feature5, feature6, feature7]])

# Normalize using StandardScaler (you'll need to fit this on your training data)
scaler = StandardScaler()
# scaler.fit(your_training_data)  # Fit on your training data
input_normalized = scaler.transform(input_data)

# Convert to tensor
input_tensor = torch.tensor(input_normalized, dtype=torch.float32)

# Inference
with torch.no_grad():
    outputs = quantized_model(input_tensor)

# Get predicted label
predicted_class = outputs.argmax(dim=1).item()

# Map label using label encoder classes
label_mapping = {0: config["label_classes"][0], 1: config["label_classes"][1]}
print(f"Predicted Gender: {label_mapping[predicted_class]}")

Performance Metrics

Model Size: Reduced through QInt8 quantization
Input Features: 7 numerical features
Output Classes: 2 (Binary gender classification)
Training Split: 80% train, 20% validation

Training Details

Dataset

The model was trained on the Gender Classification Dataset v7, featuring:

7 numerical input features
Binary gender classification labels
Preprocessed and normalized data

Training Configuration

Epochs: 10
Batch Size: 32
Learning Rate: 0.001
Optimizer: Adam
Loss Function: CrossEntropyLoss
Normalization: StandardScaler

Model Architecture

Input Layer: 7 features
Hidden Layer: 32 neurons with ReLU activation
Output Layer: 2 neurons (binary classification)
Total Parameters: Approximately 288 parameters

Quantization

Post-training dynamic quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency with QInt8 precision.

Repository Structure

.
├── quantized_model/
│   ├── config.json              # Model configuration
│   ├── pytorch_model.bin        # Quantized model weights
│   ├── model.safetensors        # Alternative model format
│   ├── vocab.txt               # Feature names
│   ├── tokenizer_config.json   # Scaler configuration
│   └── special_tokens_map.json # Label encoder metadata
├── gender-classification.ipynb  # Training notebook
└── README.md                   # Model documentation

Input Features

The model expects 7 numerical features as input. The exact feature names and preprocessing requirements are stored in the configuration files.

Limitations

The model is designed for binary gender classification only
Performance depends on the similarity between inference data and training data distribution
Quantization may result in minor accuracy changes compared to full-precision models
Requires proper feature scaling using StandardScaler fitted on training data

Contributing

Contributions are welcome! Feel free to open an issue or PR for improvements, fixes, or feature extensions.