| # 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 | |
| ```bash | |
| pip install torch pandas scikit-learn numpy | |
| ``` | |
| ### Loading the Quantized Model | |
| ```python | |
| 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. | |
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