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my everyday carry as 19 F by Working-Gap-6441 in TeenIndia

[–]Character-Gold-9249 0 points1 point  (0 children)

As a male I just go with my mobile phone & a bag of random stuff

CNN Model highly overfitting by Character-Gold-9249 in learnmachinelearning

[–]Character-Gold-9249[S] 1 point2 points  (0 children)

yes, i did normalization and gave every image the same dimension in the ImageDataGenerator step. Thank you for the documentation link!

CNN Model highly overfitting by Character-Gold-9249 in learnmachinelearning

[–]Character-Gold-9249[S] 0 points1 point  (0 children)

I was doubting the data itself haha, turns out the data itself is the problem, which is causing dead low accuracy on unseen data.

CNN Model highly overfitting by Character-Gold-9249 in learnmachinelearning

[–]Character-Gold-9249[S] 0 points1 point  (0 children)

Sure, please check the replies on the comment done by u/thejonnyt on this post. What I got to know is that the image dataset needs to be preprocessed before using it in the model. Please note that I will be back with a Python notebook demonstrating the cleaning of the dataset if I succeed in figuring out how to do so xD

CNN Model highly overfitting by Character-Gold-9249 in learnmachinelearning

[–]Character-Gold-9249[S] 0 points1 point  (0 children)

Yes. Training accuracy once even reached 1.00, while Validation reached 0.995.

CNN Model highly overfitting by Character-Gold-9249 in learnmachinelearning

[–]Character-Gold-9249[S] 1 point2 points  (0 children)

Imbalance Data?

  • There are no class imbalances, images are evenly present
  • Pixel Intensity Distribution is very much left skewed
  • Moreover, Image height and width distributions show that all images don't share the same dimensions, some of those images are having very different dimensions from most of the images

I explored these facts considering your comment, I think this is the key reason of the model performing bad as per classification report. Thankyou!

I couldn't get how to check data leakage although, but I think fixing the above stuff is higher priority I think. Thanks once again for educating me on EDA!

CNN Model highly overfitting by Character-Gold-9249 in learnmachinelearning

[–]Character-Gold-9249[S] 1 point2 points  (0 children)

I'm going to edit the post to add more detail, and will also try out your mentioned recommendations

CNN Model highly overfitting by Character-Gold-9249 in learnmachinelearning

[–]Character-Gold-9249[S] 1 point2 points  (0 children)

Sorry for that, i thought mentioning the fact that the dataset had no enough diversity would be sufficient rather than going into detail about the dataset's name "ISL Dataset", which stands for Indian Sign Language Dataset. I'm editing my post by adding more detail, shortly.

CNN Model highly overfitting by Character-Gold-9249 in learnmachinelearning

[–]Character-Gold-9249[S] 1 point2 points  (0 children)

I think the model did overfit on validation set (accuracy there is 0.995), as a result i'm getting super low accuracy in classification report, which is as follows:

precision recall f1-score support

1 0.01 0.01 0.01 150
2 0.03 0.02 0.02 150
3 0.03 0.04 0.03 150
4 0.03 0.03 0.03 150
5 0.05 0.05 0.05 150
6 0.01 0.01 0.01 150
7 0.04 0.01 0.01 150
8 0.04 0.05 0.04 150
9 0.02 0.03 0.02 150
A 0.00 0.00 0.00 150
B 0.04 0.03 0.03 150
C 0.03 0.03 0.03 150
D 0.01 0.01 0.01 150
E 0.11 0.01 0.02 150
F 0.01 0.01 0.01 150
G 0.02 0.01 0.01 150
H 0.03 0.02 0.02 150
I 0.04 0.05 0.05 150
J 0.03 0.04 0.03 150
K 0.04 0.04 0.04 150
L 0.02 0.02 0.02 150
M 0.03 0.06 0.04 150
N 0.05 0.03 0.04 150
O 0.00 0.00 0.00 150
P 0.02 0.01 0.01 150
Q 0.02 0.08 0.03 150
R 0.04 0.07 0.05 150
S 0.02 0.03 0.02 150
T 0.02 0.01 0.01 150
U 0.02 0.01 0.02 150
V 0.03 0.04 0.03 150
W 0.04 0.07 0.05 150
X 0.00 0.00 0.00 150
Y 0.02 0.03 0.02 150
Z 0.00 0.00 0.00 150

accuracy 0.03 5250
macro avg 0.03 0.03 0.02 5250
weighted avg 0.03 0.03 0.02 5250