Building and Training a CNN Model with Flask and PyTorch | Full Project Walkthrough

In this video, we walk through a complete project where we build, train, and monitor a Convolutional Neural Network (CNN) model using PyTorch, integrated with a Flask web application for real-time monitoring. This project is designed to train models on the MNIST dataset and visualize training metrics such as loss and accuracy.
What You'll Learn:
How to build a CNN model using PyTorch.
Setting up a Flask web server to manage and monitor multiple models.
Real-time logging of training progress, including loss and accuracy metrics.
Saving and loading model configurations and results.
Displaying test results with predictions on sample images from the MNIST dataset.
Using threading to handle multiple model training sessions concurrently.

Project Features:
CNN Architecture: A simple CNN with 4 convolutional layers, ReLU activations, max pooling, and dropout for regularization.
Training Monitor: A Flask-based web interface that allows you to configure models, start training, and view real-time metrics such as loss, accuracy, and test results.
Logging & History: The system saves training logs and test results for each model configuration, allowing you to track the performance of different models over time.
Concurrency: The project uses threading to handle multiple models in the training queue simultaneously.

Technologies Used:
PyTorch for building and training the CNN model.
Flask for creating the web application interface.
TQDM for progress bars during training.
Matplotlib for plotting training metrics.
JSON for saving logs and results.

Видео Building and Training a CNN Model with Flask and PyTorch | Full Project Walkthrough канала Amit Joshi