diff --git a/.gitignore b/.gitignore
index 6333025..d45e338 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,3 +6,5 @@ checkpoint
 *.npy
 model.ckpt-*
 
+snapshots/*
+*.sh
\ No newline at end of file
diff --git a/network.py b/network.py
index 1ea5939..7deb67a 100644
--- a/network.py
+++ b/network.py
@@ -73,10 +73,12 @@ def create_session(self):
         self.sess.run([global_init, local_init])
         
     def restore(self, data_path, var_list=None):
+        if var_list is None:
+            var_list = tf.global_variables()
         if data_path.endswith('.npy'):
-            self.load_npy(data_path, self.sess)
+            self.load_npy(data_path, self.sess, var_list=var_list)
         else:
-            loader = tf.train.Saver(var_list=tf.global_variables())
+            loader = tf.train.Saver(var_list=var_list)
             loader.restore(self.sess, data_path)
         
         print('Restore from {}'.format(data_path))
@@ -92,13 +94,16 @@ def save(self, saver, save_dir, step):
         print('The checkpoint has been created, step: {}'.format(step))
 
     ## Restore from .npy
-    def load_npy(self, data_path, session, ignore_missing=False):
+    def load_npy(self, data_path, session, ignore_missing=False, var_list=None):
         '''Load network weights.
         data_path: The path to the numpy-serialized network weights
         session: The current TensorFlow session
         ignore_missing: If true, serialized weights for missing layers are ignored.
         '''
+        if var_list is None:
+            var_list = tf.global_variables()
         data_dict = np.load(data_path, encoding='latin1').item()
+        var_names = [v.name for v in var_list]
         for op_name in data_dict:
             with tf.variable_scope(op_name, reuse=True):
                 for param_name, data in data_dict[op_name].items():
@@ -107,6 +112,9 @@ def load_npy(self, data_path, session, ignore_missing=False):
                             param_name = BN_param_map[param_name]
 
                         var = tf.get_variable(param_name)
+                        if var.name not in var_names:
+                            print("Not restored: %s" % var.name)
+                            continue
                         session.run(var.assign(data))
                     except ValueError:
                         if not ignore_missing:
diff --git a/utils/image_reader.py b/utils/image_reader.py
index 3d7a65e..ec7d623 100644
--- a/utils/image_reader.py
+++ b/utils/image_reader.py
@@ -100,6 +100,7 @@ def _random_crop_and_pad_image_and_labels(image, label, crop_h, crop_w, ignore_l
     combined_crop = tf.random_crop(combined_pad, [crop_h, crop_w, 4])
     img_crop = combined_crop[:, :, :last_image_dim]
     label_crop = combined_crop[:, :, last_image_dim:]
+    label_crop = label_crop + ignore_label
     label_crop = tf.cast(label_crop, dtype=tf.uint8)
 
     # Set static shape so that tensorflow knows shape at compile time.
@@ -133,10 +134,18 @@ def _infer_preprocess(img, swap_channel=False):
         
     return img, o_shape, n_shape
 
-def _eval_preprocess(img, label, shape, dataset):
-    if dataset == 'cityscapes':
+def _eval_preprocess(img, label, shape, dataset, ignore_label=255):
+    if 'citycapes' in dataset:
         img = tf.image.pad_to_bounding_box(img, 0, 0, shape[0], shape[1])
         img.set_shape([shape[0], shape[1], 3])
+
+        label = tf.cast(label, dtype=tf.float32)
+        label = label - ignore_label # Needs to be subtracted and later added due to 0 padding.
+        label = tf.image.pad_to_bounding_box(label, 0, 0, shape[0], shape[1])
+        label = label + ignore_label
+        label = tf.cast(label, dtype=tf.uint8)
+        label.set_shape([shape[0], shape[1], 1])
+
     else:
         img = tf.image.resize_images(img, shape, align_corners=True)
      
@@ -178,7 +187,7 @@ def create_tf_dataset(self, cfg):
             
         else: # Evaluation phase            
             dataset = dataset.map(lambda x, y: 
-                                  _eval_preprocess(x, y, cfg.param['eval_size'], cfg.dataset),
+                                  _eval_preprocess(x, y, cfg.param['eval_size'], cfg.dataset, cfg.param['ignore_label']),
                                   num_parallel_calls=cfg.N_WORKERS)
             dataset = dataset.batch(1)