This repository contains code and models for ArchIScan project for sherd recognition.
Models can be found in Releases. We publish two models: OnSimulData and OnRealData. Both models have the same architecture:
net =InceptionV3(weights='imagenet', include_top=False, input_shape=(img_height,img_width,3))
net.trainable = True# all layers trainable
model = Sequential([ # extension
net,
# here is our custom prediction layers
Dropout(0.50),
GlobalAveragePooling2D(),
Dense(512, activation='relu'),
Dense(NUM_CLASSES, activation='softmax')
])
We used architecture InceptionV3 without fully connected layers.
Input size of image is img_height=img_width=224
All layers are trainable.
After convolutional pare of InceptionV3 we added four layers: Dropout(0.50), GlobalAveragePooling2D(), Dense(512, activation='relu'), and Dense(NUM_CLASSES, activation='softmax').
NUM_CLASSES=5.
OnSimulData is trained on simulated data only: Inception V3 network, trained on simulated sherds, sizes of the sherds 10-30% of the whole vessels, trained for 10 epochs before overfitting.
OnRealData is OnSimulData additionally trained on real data: Inception V3 network, fine-tuned on training set of 5 real classes (25 epochs)
To access models you should click word "Releases" and then select one of two models.
Code for training of CNN can be found in ???
Code to simulate artificial sherds can be found in ???