Challenge in Achieving Good Results with Limited CNN Model and Small Dataset
Analysis
This post highlights the difficulty of achieving satisfactory results when training a Convolutional Neural Network (CNN) with significant constraints. The user is limited to single layers of Conv2D, MaxPooling2D, Flatten, and Dense layers, and is prohibited from using anti-overfitting techniques like dropout or data augmentation. Furthermore, the dataset is very small, consisting of only 1.7k training images, 550 validation images, and 287 testing images. The user's struggle to obtain good results despite parameter tuning suggests that the limitations imposed may indeed make the task exceedingly difficult, if not impossible, given the inherent complexity of image classification and the risk of overfitting with such a small dataset. The post raises a valid question about the feasibility of the task under these specific constraints.
Key Takeaways
- •Small datasets and restrictive model architectures can severely limit achievable accuracy.
- •Anti-overfitting techniques are crucial for training effective models, especially with limited data.
- •Experimentation with parameters alone may not be sufficient to overcome fundamental limitations in model architecture and data size.
“"so I have a simple workshop that needs me to create a baseline model using ONLY single layers of Conv2D, MaxPooling2D, Flatten and Dense Layers in order to classify 10 simple digits."”