Why is it called a "Simple CNN"?
It's called a "Simple CNN" because it's a relatively shallow and straightforward network that we've built from scratch. It has a small number of convolutional and dense layers, and it's designed specifically for this helmet detection task. In contrast to more complex models, it has a simple architecture and is not pre-trained on any other data.
Disadvantages of the Simple CNN compared to other models:
Here's a comparison of the Simple CNN to the more advanced models you mentioned:
1. Simple CNN vs. VGG-16 (Base)
Learning from Scratch: The Simple CNN has to learn to recognize features (like edges, corners, and textures) entirely from the helmet dataset. This can be challenging, especially with a relatively small dataset.
VGG-16's Pre-trained Knowledge: VGG-16, on the other hand, is a very deep network that has already been trained on the massive ImageNet dataset (which has millions of images and 1,000 different classes). This pre-training has taught VGG-16 to recognize a vast library of visual features. By using the VGG-16 "base" (the convolutional layers), we are essentially using it as a powerful feature extractor. This is a form of transfer learning, and it often leads to much better performance than a simple CNN, especially when you don't have a lot of data.
2. Simple CNN vs. VGG-16 + FFNN (Feed-Forward Neural Network)
Customization for the Task: Adding a custom FFNN (which is just a set of dense layers) on top of the VGG-16 base allows us to take the powerful features extracted by VGG-16 and fine-tune them specifically for our helmet detection task. This combination often leads to even better performance than just using the VGG-16 base alone.
Limited Learning Capacity: The Simple CNN has a much smaller dense layer, which limits its ability to learn complex patterns from the features it extracts.
3. Simple CNN vs. VGG-16 + FFNN + Data Augmentation
Overfitting: With a small dataset, a Simple CNN is highly prone to overfitting. This means it might learn the training data very well but fail to generalize to new, unseen images.
Robustness through Data Augmentation: Data augmentation artificially expands the training dataset by creating modified versions of the existing images (e.g., rotating, shifting, or zooming them). This helps to make the model more robust and less likely to overfit. When you combine data augmentation with a powerful pre-trained model like VGG-16 and a custom FFNN, you are using a very powerful and effective technique for image classification.
In summary, the main disadvantages of the Simple CNN are:
It has to learn everything from scratch, which requires a lot of data.
It's more prone to overfitting.
It's less powerful than pre-trained models like VGG-16, which have already learned a rich set of features from a massive dataset.
For these reasons, using a pre-trained model like VGG-16 is often the preferred approach for image classification tasks, especially when you have a limited amount of data.
