Seasonal Mood
For this first project, I wanted to try something a bit more abstract than just objects. Here, I train a model to classify the four seasons from images obtained from Duck Duck Go:
- Spring
- Summer
- Fall
- Winter
In terms of hyperparamaters, here are some adjustments that seemed helpful:
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Architecture: I tried resnet18, resnet34, resnet50 and resnet101. With smaller training sets of 100-200 images/season, the smaller architectures outpreformed the larger ones—as high as 90-95% accuracy. I pursume this might in part be due to overtraining. Additionally, the smaller sets of images are likely have been more homogeneous than the larger training sets. For the final model I used resnet101 and 750 images/season with resulting accurcay of 80-85%.
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Number of Pictures: 150 training images/season were initially used. These trained very well. As I increased the training sizes, performance dropped. I believe that this is because the larger training sets were much more diverse. I could verify this by looking manually at the DDG search results: later images diverged significantly from earlier images. For the final model, I used 750 training images/season. These larger training sets did not perform as well. Hopefully, the larger and more diverse training sets will perform better
In the Wild, with real test data. -
Image Size: 400 size images performed better than 300 size images
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Transformations: squish outperformed crop
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Training Epochs: 5 training epochs with resnet101 showed continued reduction in both training and validation losses