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README.md

@@ -1,74 +1,48 @@
-# AI Model Training Project
+---
+language: en
+license: mit
+tags:
+- tensorflow
+- image-classification
+- mnist
+- digits
+datasets:
+- mnist
+metrics:
+- accuracy
+---
 
-This project demonstrates a complete machine learning workflow from data preparation to model deployment, using the MNIST dataset with an innovative approach to digit recognition.
+# Digit Recognition Model
 
-## Project Structure
+This model is trained to recognize handwritten digits from the MNIST dataset.
 
-```
-.
-├── data/                  # Dataset storage
-├── models/               # Saved model files
-├── src/                  # Source code
-│   ├── data_preparation.py
-│   ├── model.py
-│   ├── training.py
-│   ├── evaluation.py
-│   └── deployment.py
-├── notebooks/           # Jupyter notebooks for exploration
-├── requirements.txt     # Project dependencies
-└── README.md           # Project documentation
-```
-
-## Setup Instructions
-
-1. Create a virtual environment:
-```bash
-python -m venv venv
-source venv/bin/activate  # On Windows: venv\Scripts\activate
-```
+## Model Description
 
-2. Install dependencies:
-```bash
-pip install -r requirements.txt
-```
-
-3. Run the training pipeline:
-```bash
-python src/training.py
-```
+- **Model Type:** CNN with Attention
+- **Task:** Image Classification
+- **Input:** 28x28 grayscale images
+- **Output:** Digit classification (0-9)
 
-## Project Features
+## Training
 
-- Custom CNN architecture for robust digit recognition
-- Data augmentation techniques
-- Model evaluation and hyperparameter tuning
-- Model deployment pipeline
-- Performance monitoring
+The model was trained on the MNIST dataset using a CNN architecture with attention mechanisms.
 
-## Learning Concepts Covered
+## Usage
 
-1. Data Preprocessing
-   - Data loading and cleaning
-   - Feature engineering
-   - Data augmentation
+```python
+import tensorflow as tf
+import numpy as np
 
-2. Model Architecture
-   - Custom CNN design
-   - Layer configuration
-   - Activation functions
+# Load the model
+model = tf.saved_model.load("path_to_saved_model")
 
-3. Training Process
-   - Loss functions
-   - Optimizers
-   - Learning rate scheduling
-   - Early stopping
+# Prepare input
+image = tf.keras.preprocessing.image.load_img("digit.png", target_size=(28, 28))
+image = tf.keras.preprocessing.image.img_to_array(image)
+image = image.astype('float32') / 255.0
+image = np.expand_dims(image, axis=0)
 
-4. Evaluation
-   - Metrics calculation
-   - Cross-validation
-   - Model comparison
-
-5. Deployment
-   - Model saving
-   - Inference pipeline
-   - Performance monitoring 
+# Make prediction
+predictions = model(image)
+predicted_digit = tf.argmax(predictions, axis=1).numpy()[0]
+```