I am using a very simple model to classify a 224x224 RGB image.
For a test, I have labelled my images (2 labels "Green" or "Red", 2,000 images of each) based on colour of a single fixed pixel from upper part of image.
My simple model achieves very high accuracy and very low loss, until I add more "random" pixels to lower part of image.
Why is my model getting confused?
The model easily copes with:
But, when I add more pixels in lower part of image - it achieves only 50% accuracy and high loss:
These project is a bit facile but I am researching a simple image structure I can control.
def VerySimpleNet(): model = keras.Sequential([ keras.layers.Conv2D(16, kernel_size=3, activation=tf.nn.relu, input_shape=(224, 224, 3)), keras.layers.Dropout(0.4), keras.layers.Flatten(), keras.layers.Dense(3, activation=tf.nn.softmax) ]) return model
Is it possible to add a bonus to Data Science questions to encourage more answers?
I would really be interested if anybody was keen to have a more interactive chat(email,txt?) with me on this question.