/g/ - Technology

I trained a cnn on this dataset

https://data.mendeley.com/datasets/b6fftwbr2v/1

[Blocked URL: https://www.sciencedirect.com/science/article/pii/S2352340921009616]

then i did feature extraction and did k means clustering and there were 4 clusters i made, now thing is, in this sample images in picrel it shows fruits in cluster 1 and 2 which is mid ripe and ripe, 0 and 3 are unripe and overripe, now that i tried this model on different images from internet, it only predicts unripe or overripe although it predicts overripe and unripe quite perfectly but never does anything for mid ripe and unripe.

>>824(OP)

Did you use pca or fca?

>>827

since the database has info about class ( unripe ovveripe etc) you should use fca it will seperate classes more which should help distinguish more.

>>828

also try xgboost on fca data , xg boost is really good . :)

thats one method test it .

another might be doing regression. on fca dataset.

>>827

another way except all this is to redefine model ,

instead of model predicting ripe unripe directly , you can divide this into two part , one model predicts what kind of fruit that is ( which has high accuracy , easy to make ) and with the output of that you decide whether that fruit is ripe unripe etc now for this part you can make model for each class using loops or whatever ( use dataset of that class only). this will be better than all other as this will work as a pigeonhole , we will have selective data to work with which can help us distinguish more.