--- a
+++ b/src/model_defs/resnet50.py
@@ -0,0 +1,48 @@
+num_chan_in = 3
+height = 512
+width = 512
+num_classes = 6
+bn_momentum = 0.99
+
+inputs = K.layers.Input([height, width, num_chan_in], name="DICOM")
+
+params = dict(kernel_size=(3, 3),
+                activation="relu",
+                padding="same",
+                kernel_initializer="he_uniform")
+
+img_1 = K.layers.BatchNormalization(momentum=bn_momentum)(inputs)
+img_1 = K.layers.Conv2D(32, **params)(img_1)
+img_1 = K.layers.MaxPooling2D(pool_size=(2,2))(img_1)
+
+img_1 = K.layers.Conv2D(64, **params)((K.layers.BatchNormalization(momentum=bn_momentum))(img_1))
+img_1 = K.layers.MaxPooling2D(name='skip1', pool_size=(2,2))(img_1)
+
+# Residual block
+img_2 = K.layers.Conv2D(128, **params) ((K.layers.BatchNormalization(momentum=bn_momentum))(img_1))
+img_2 = K.layers.Conv2D(64, name='img2', **params) ((K.layers.BatchNormalization(momentum=bn_momentum))(img_2))
+img_2 = K.layers.add( [img_1, img_2] )
+img_2 = K.layers.MaxPooling2D(name='skip2', pool_size=(2,2))(img_2)
+
+# Residual block
+img_3 = K.layers.Conv2D(128, **params)((K.layers.BatchNormalization(momentum=bn_momentum))(img_2))
+img_3 = K.layers.Conv2D(64, name='img3', **params)((K.layers.BatchNormalization(momentum=bn_momentum))(img_3))
+img_res = K.layers.add( [img_2, img_3] )
+
+# Filter residual output
+img_res = K.layers.Conv2D(128, **params)((K.layers.BatchNormalization(momentum=bn_momentum))(img_res))
+
+# Tendancy to flatten
+img_res = K.layers.GlobalMaxPooling2D(name='global_pooling') ( img_res )
+
+dense1 = K.layers.Dropout(0.5)(K.layers.Dense(256, activation = "relu")(img_res)) 
+dense2 = K.layers.Dropout(0.5)(K.layers.Dense(64, activation = "relu")(dense1)) 
+prediction = K.layers.Dense(num_classes, activation = 'sigmoid')(dense2)
+
+model = K.models.Model(inputs=[inputs], outputs=[prediction])
+
+opt = K.optimizers.Adam(lr = 1e-3, beta_1 = .9, beta_2 = .999, decay = 1e-3)
+
+model.compile(loss=loss.weighted_log_loss(),
+                optimizer=opt,
+                metrics = [loss.weighted_loss()])
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