Hello :) Today is Day 37!
A quick summary of today:
- Simple PyTorch animal classification
- Traffic sign classification (and localisation)
Firstly
to put knowledge into practice I decided to try to make an animal classification model
There are some limitations with Kaggle’s GPU so I had to tone down the picture sizes, the augmentations and model complexity.
So ~ data augmentation
Image example:
The categories: cat, dog, elephant, horse, lion
I divided the data into batches:
The model is:
The training took a lot of time; below you can see the last epoch’s results
Example classification:
(there are some weird classifications too haha)
**Next ~ **
About the traffic sign classification and localisation model
Here is a link to the notebook on Kaggle.
The model’s dataset has 43 classes (here are 21 of them)
The image size varied a lot so I standardised it
Model architecture:
I tried various architectures, but the accuracy was not that good, so online I found AlexNet and wow ~
Example:
I applied what I learned yesterday to this model according to the vegetable/apple bounding box model from yesterday, but the accuracy and loss won’t decrease even after training, and I’ll try again tomorrow!!!
Now it’s 2.37am and I will go to bed ~
That is all for today!
See you tomorrow :)