I followed the steps mentioned in the README. After I downloaded the dataset and placed it in the right directory, I used FgSegNet.py to start the training. Pasting below the error log:
(50, 240, 320, 3) (50, 120, 160, 3) (50, 60, 80, 3)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/FgSegNet/FgSegNet/FgSegNet.py in <module>()
219
220 results = generateData(train_dir, dataset_dir, scene)
--> 221 train(results, scene, mdl_path, log_dir, vgg_weights_path)
222 del results
/FgSegNet/FgSegNet/FgSegNet.py in train(results, scene, mdl_path, log_dir, vgg_weights_path)
141 img_shape = results[0][0].shape
142 model = FgSegNetModule(lr, reg, img_shape, scene, vgg_weights_path)
--> 143 model = model.initModel()
144
145 tb = keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=0, batch_size=batch_size, write_graph=False, write_grads=True, write_images=True, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None)
/content/FgSegNet/FgSegNet/FgSegNetModule.py in initModel(self)
85
86 input_1 = Input(shape=(h, w, d), name='ip_scale1')
---> 87 vgg_layer_output = self.VGG16(input_1)
88 shared_model = Model(inputs=input_1, outputs=vgg_layer_output, name='shared_model')
89 shared_model.load_weights(self.vgg_weights_path, by_name=True)
/content/FgSegNet/FgSegNet/FgSegNetModule.py in VGG16(self, x)
27
28 # Block 1
---> 29 x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', data_format='channels_last')(x)
30 x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
31 x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 return wrapper
90 return legacy_support
---> 91
92
93 generate_legacy_method_interface = functools.partial(generate_legacy_interface,
/usr/local/lib/python3.6/dist-packages/keras/layers/convolutional.py in __init__(self, filters, kernel_size, strides, padding, data_format, dilation_rate, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, **kwargs)
439 bias_constraint=None,
440 **kwargs):
--> 441 super(Conv2D, self).__init__(
442 rank=2,
443 filters=filters,
TypeError: super(type, obj): obj must be an instance or subtype of type