LabVIEW and PyTorch Comparison

Description

This project presents a comparison of handwritten digit recognition performance between LabView and PyTorch, utilizing a Convolutional Neural Network (CNN) model.

CNN Architecture

Comparison Results

Below is a detailed table comparing various metrics between LabVIEW and PyTorch implementations.

Metric	LabView NI Value	PyTorch Value	Unit	Description
Model Accuracy
Final Test Accuracy	99.22	99.38	%	Accuracy achieved on the test set after training.
Training Accuracy (at final epoch)	99.39	99.21	%	Accuracy on the training set at the final epoch.
Validation Accuracy (at final epoch)	99.37	99.16	%	Accuracy on the validation set at the final epoch.
Training Time
Time per Epoch	46.5	33.6	Seconds/Epoch	Time taken to complete one epoch of training.
Total Training Time	696	504	Seconds	Total time taken to train the model over all epochs.
Average Time per Batch	0.104	0.0716	Seconds/Batch	Average time taken to process a single batch during training.
Memory Usage
Maximum Memory Usage	396.4 MB	641.47 MB	MB or GB	Peak memory usage during training (RAM).
Average Memory Usage	383 MB	595.9 MB	MB or GB	Average memory consumption during training.
Inference Time
Time per Batch (Inference)	0.57	0.012685	Seconds/Batch	Time taken to make predictions on a batch during inference.
Time per Sample (Inference)	0.00253	0.000791	Seconds/Sample	Average time taken to make a prediction on a single sample.
Resource Usage
CPU Utilization During Training	7	5.5	%	Percentage of CPU usage during training.
CPU Utilization During Inference	2	1.8	%	Percentage of CPU usage during inference.
Model Convergence
Number of Epochs to Convergence	15	15	Count	Number of epochs until the model converges to a stable accuracy level.
Final Training Loss	0.00509	0.0079	-	Final loss on the training set after training completes.
Final Validation Loss	0.0249	0.0203	-	Final loss on the validation set after training completes.

Loss Over Epochs

The following plots demonstrate the change in model loss over each epoch during training.

PyTorch Loss Over Epochs

LabLabView Loss Over EpochsView

Validation Accuracy Over Epochs - PyTorch vs Labview