Welcome back to the overview series! In the fourth instalment the final results from the computer vision model is discussed.
Our final iteration of the computer vision model is a ResNet50 multi-class classification model that classifies between cotton, denim, and polyester. During training, regularization and data augmentations (blur, affine, etc) were used to artificially increase the data size and avoid overfitting. The computer vision model achieved an accuracy of 80.83% on the test set which is comprised of 10% of the fabrics dataset for cotton, denim, and polyester. Due to the Identifybre camera apparatus being different from the one used to collect the training data, in practice the model accuracy is somewhat lower. Being able to invest in a better camera and also collect a much larger dataset to train on would drastically improve the accuracy! For the prototype these are definitely promising results.
The first figure is that of a confusion matrix of the test set and the second figure showcases the difference in the camera quality.
Identifybre 