diff --git a/libs/configs/config_v1.py b/libs/configs/config_v1.py
index 6f7a996..5a2ddcb 100644
--- a/libs/configs/config_v1.py
+++ b/libs/configs/config_v1.py
@@ -11,6 +11,10 @@
     'train_dir', './output/mask_rcnn/',
     'Directory where checkpoints and event logs are written to.')
 
+tf.app.flags.DEFINE_integer(
+    'train_checkpoint_interval', 3000,
+    'The number of steps per saved checkpoint.')
+
 tf.app.flags.DEFINE_string(
     'pretrained_model', './data/pretrained_models/resnet_v1_50.ckpt',
     'Path to pretrained model')
@@ -42,7 +46,7 @@
     'The name of the train/test/val split.')
 
 tf.app.flags.DEFINE_string(
-    'dataset_dir', 'data/coco/',
+    'dataset_dir', './data/coco/',
     'The directory where the dataset files are stored.')
 
 tf.app.flags.DEFINE_integer(
@@ -130,7 +134,7 @@
     'Specifies how the learning rate is decayed. One of "fixed", "exponential",'
     ' or "polynomial"')
 
-tf.app.flags.DEFINE_float('learning_rate', 0.002,
+tf.app.flags.DEFINE_float('learning_rate', 2e-3
                           'Initial learning rate.')
 
 tf.app.flags.DEFINE_float(
@@ -143,8 +147,8 @@
 tf.app.flags.DEFINE_float(
     'learning_rate_decay_factor', 0.94, 'Learning rate decay factor.')
 
-tf.app.flags.DEFINE_float(
-    'num_epochs_per_decay', 2.0,
+tf.app.flags.DEFINE_integer(
+    'num_epochs_per_decay', 2,
     'Number of epochs after which learning rate decays.')
 
 tf.app.flags.DEFINE_bool(
diff --git a/train/train.py b/train/train.py
index 839c178..f327a7d 100644
--- a/train/train.py
+++ b/train/train.py
@@ -63,21 +63,23 @@ def solve(global_step):
 
 def restore(sess):
      """choose which param to restore"""
+     is_restored = False
      if FLAGS.restore_previous_if_exists:
         try:
             checkpoint_path = tf.train.latest_checkpoint(FLAGS.train_dir)
             restorer = tf.train.Saver()
             restorer.restore(sess, checkpoint_path)
+            is_restored = True
             print ('restored previous model %s from %s'\
                     %(checkpoint_path, FLAGS.train_dir))
             time.sleep(2)
             return
         except:
-            print ('--restore_previous_if_exists is set, but failed to restore in %s %s'\
+            print ('--restore_previous_if_exists is set, but FAILED TO RESTORE in %s %s'\
                     % (FLAGS.train_dir, checkpoint_path))
             time.sleep(2)
 
-     if FLAGS.pretrained_model:
+     if FLAGS.pretrained_model and not is_restored:
         if tf.gfile.IsDirectory(FLAGS.pretrained_model):
             checkpoint_path = tf.train.latest_checkpoint(FLAGS.pretrained_model)
         else:
@@ -105,6 +107,8 @@ def restore(sess):
 def train():
     """The main function that runs training"""
 
+    print("Starting learning rate %.7f"%(FLAGS.learning_rate))
+
     ## data
     image, ih, iw, gt_boxes, gt_masks, num_instances, img_id = \
         datasets.get_dataset(FLAGS.dataset_name, 
@@ -143,6 +147,7 @@ def train():
 
     ## solvers
     global_step = slim.create_global_step()
+    # global_step = tf.Variable(0, name='global_step', trainable=False)
     update_op = solve(global_step)
 
     cropped_rois = tf.get_collection('__CROPPED__')[0]
@@ -208,10 +213,10 @@ def train():
             summary_str = sess.run(summary_op)
             summary_writer.add_summary(summary_str, step)
 
-        if (step % 10000 == 0 or step + 1 == FLAGS.max_iters) and step != 0:
+        if (step % FLAGS.train_checkpoint_interval == 0 or step + 1 == FLAGS.max_iters) and step != 0:
             checkpoint_path = os.path.join(FLAGS.train_dir, 
                                            FLAGS.dataset_name + '_' + FLAGS.network + '_model.ckpt')
-            saver.save(sess, checkpoint_path, global_step=step)
+            saver.save(sess, checkpoint_path, global_step=global_step)
 
         if coord.should_stop():
             coord.request_stop()