synthetic-pattern-classifier

Model Description

This model is a simple Convolutional Neural Network (CNN) designed to classify 3-channel 32×32 images into three categories of synthetic patterns. The architecture consists of a Conv2d layer followed by ReLU activation, a MaxPool layer, and finally a Linear layer. The model is built using the PyTorch framework.

How to Use

To use this model, you can load it using torch.load. This will allow you to make predictions on new, unseen data.

Training Details

The model was trained on a dataset of synthetic images for 5 epochs using the Adam optimizer and CrossEntropyLoss as the loss function. The training process was designed to optimize the model's performance on the specific task of distinguishing between the three categories of synthetic patterns.

Limitations

This model is limited by the fact that it was trained on synthetic data, which may not generalize well to real-world images. The model's performance may degrade when faced with images that have different characteristics than those in the training dataset. Additionally, the simple architecture of the model may not be sufficient to capture complex patterns in more diverse datasets.