Famous dataset used for vision learning, PETS. Images of varying sizes of different animals The images are named with the spesific animal's name and race(Model's target label), i.e cocker_spaniel_19.jpg
To save on time and computing, allowing to run multiple models on more epochs
Hypothesis: Squish will perform well on square images
Using crop and resizing method as mentioned before
Using btch_tfms to transform the pictures with a multiplier from 0 to 2.0
More info about augmentation, a good notebook is Introduction to Image Augmentation using fastai by Sanyam Bhutani
Dataloaders with augmentation multiplier with values 0.1, 0.5, 1.0, 1.5 and 2.0
Using Resnet18 with 10 epochs
After 5 epochs augmented data performs better
Used Jeremy Howard's Notebook to find different architectures to try out
Limited which models avalible due to computing power and image size
Exploring the different levels of resnet-architectures, but including one model from different architecture
Surprising how bad efficinet is performing
From the early testing, resnet26 seems to perform the best. Will be chosen as the benchmark.
Impact does not seem that significiant. Suspecting its due to image size, so limited how much levels of augmentation and change there is to be had
Codeblocks are the same for the resnet18 model, with 'resnet26' replaced with 'resnet18'
| resnet26 | resnet18 |
|---|---|
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Dihedral
