**VIEW THE CODE AND RESOLVE THE ISSUE
**
https://drive.google.com/file/d/1PDNihjdqw-N3Ud-3Reo4Q6oYL2cHcSOY/view?usp=sharing
Hi @Palak_Talreja, If you see the training and validation accuracy of the model, training accuracy is far greater than validation accuracy. so we can conclude that the model is overfitting. Due to this you are getting the performance difference.
Generally to overcome overfitting there are few techniques to overcome this problem
- Data augmentation: If the training set has less number of images you use data augmentation to improve the samples of the dataset.
- Using DropOut Layer: When you apply dropout to a layer, it randomly drops out a number of output units from the layer during the training process.
- Adding L1 / L2 regularization to the model.
- Early stopping: Once the validation loss stops decreasing but rather begins increasing, we stop the training and save the current model.
Thank You.