Getting Wrong output even though vgg16 model showing 95% val_accuracy

[Avataryug-hs]

In this project i have been trying to classify 3 hairstyles - Braided , curly and straight …i am using transfer learning to classify the images … vvg16 model is giving roughly same training and testing accuracy … but when trying to predict actual image , its giving wrong result ( predicting different class)…
My Dataset is roughly 400 images for training and 100 for testing ( all are evenly divided )

Please suggest something , and help me out
Attaching model code -
`from keras.applications import VGG16

Load the pre-trained VGG16 model without the top (fully connected) layers

base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

Freeze the base model layers so they are not trainable

for layer in base_model.layers:
layer.trainable = False

Create a new model by adding custom top layers on top of the base model

model = Sequential()
model.add(base_model)
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.20))
model.add(Dense(3, activation='softmax')) # Assuming 3 classes

Compile the model

model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])

Print the model summary

model.summary()`

[SuryanarayanaY]

Hi @Avataryug-hs ,

Since you are using same dataset for training and testing and getting good val_accuracy also I would except the model to perform well. If you are using a new dataset for predictions and not getting good accuracy that means either the new dataset is having different probability distribution or trained model might be overfitting.

[Avataryug-hs]

Hi I am not using same dataset for training and testing, both contain different images, also trained model is not overfitting by the graph i got , loss functions are seeming to decrease .. attaching the screenshot . [cid:5480370a-567d-47b4-acd3-d894c58cbced]

…

________________________________ From: Surya ***@***.***> Sent: 25 April 2024 09:52 AM To: keras-team/keras ***@***.***> Cc: Harsh Shinde ***@***.***>; Mention ***@***.***> Subject: Re: [keras-team/keras] Getting Wrong output even though vgg16 model showing 95% val_accuracy (Issue #19612) Hi @Avataryug-hs<https://github.com/Avataryug-hs> , Since you are using same dataset for training and testing and getting good val_accuracy also I would except the model to perform well. If you are using a new dataset for predictions and not getting good accuracy that means either the new dataset is having different probability distribution or trained model might be overfitting. — Reply to this email directly, view it on GitHub<#19612 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/BG7XZS44RUKSAK5VPAKCZY3Y7CAJHAVCNFSM6AAAAABGYCGWKCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDANZWGMZTEMRVGU>. You are receiving this because you were mentioned.Message ID: ***@***.***>

[td-jakubl]

Could you provide code for loading and creating datasets and for model's predictions?