DenseNet-Implementation

This repo trains a DenseNet-121 model on the MNIST dataset. The dataset is resized and normalized to fit the DenseNet model, and training is done using PyTorch.

DenseNet Architecture

Project Steps

1. Import Required Libraries

To set up the environment, use the following commands:

conda create --name pytorch_env python=3.11.9 --file requirements.txt
conda activate pytorch_env

2. Load the MNIST Dataset

• Dataset is loaded using torchvision.datasets.MNIST.
• Images are resized to 224x224, normalized, and converted to tensors.

3. Split the Dataset

The combined training and test dataset is split into:

• Training Set: 42,000 samples
• Validation Set: 14,000 samples
• Test Set: 14,000 samples

4. Load Pre-trained DenseNet

• DenseNet-121 is loaded and modified for single-channel input and 10 output classes.
• Model is moved to GPU if available.

5. Define Loss Function and Optimizer

• Loss Function: CrossEntropyLoss
• Optimizer: Adam with learning rate 0.001

Results

• Test Accuracy: The accuracy achieved on the test set is 99.28%
  
  

• Confusion Matrix: A heatmap of the confusion matrix displays the model's performance across all classes.
  
  

References

• Densely Connected Convolutional Networks
• DenseNet - PyTorch