Target_size image_gen.flow_from_dataframe InceptionResNetV2 (Keras) for smaller images
You can either define input image size in ImageDataGenerator (target_size=(224,224)) or in tf.keras.layers.Input( shape=input_shape ) part.
I have a dataset (Tiny ImageNet) where the images are 64 x 64 but I want to use the pre-trained model: InceptionResNetV2 since the accuracy on all other models is low.
Can I double the dimensions in target_size to 128, 128 in the image_gen.flow_from_dataframe function and use the output in the InceptionResNetV2?
Will doubling the target size influence the training? I mean will it stretch the images or add blank space around them.
Is this way appropriate or is there a better way to train tiny images using InceptionResNetV2?