README.md

metadata

title: Digit Recognition with CNN
emoji: 🔢
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 4.19.2
app_file: app.py
pinned: false

Digit Recognition Model

This model is trained to recognize handwritten digits from the MNIST dataset.

Model Description

• Model Type: CNN with Attention
• Task: Image Classification
• Input: 28x28 grayscale images
• Output: Digit classification (0-9)

Training

The model was trained on the MNIST dataset using a CNN architecture with attention mechanisms.

Usage

import tensorflow as tf
import numpy as np

# Load the model
model = tf.saved_model.load('https://huggingface.co/nivashuggingface/digit-recognition/resolve/main/saved_model')

# 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)

# Make prediction
predictions = model(image)
predicted_digit = tf.argmax(predictions, axis=1).numpy()[0]

AI Model Training Project

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.

Project Structure

.
├── 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:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Run the training pipeline:

python src/training.py

Project Features

• Custom CNN architecture for robust digit recognition
• Data augmentation techniques
• Model evaluation and hyperparameter tuning
• Model deployment pipeline
• Performance monitoring

Learning Concepts Covered

1. Data Preprocessing

   • Data loading and cleaning
   • Feature engineering
   • Data augmentation

2. Model Architecture

   • Custom CNN design
   • Layer configuration
   • Activation functions

3. Training Process

   • Loss functions
   • Optimizers
   • Learning rate scheduling
   • Early stopping

4. Evaluation

   • Metrics calculation
   • Cross-validation
   • Model comparison

5. Deployment

   • Model saving
   • Inference pipeline
   • Performance monitoring