diff --git a/data/config.py b/data/config.py
index 81e61db1c..2480278c0 100644
--- a/data/config.py
+++ b/data/config.py
@@ -66,13 +66,9 @@ def replace(self, new_config_dict):
     'type': ResNetBackbone,
     'args': ([3, 4, 23, 3],),
 
-    'selected_layers': list(range(2, 7)),
-    'pred_scales': [[1, 2], [2], [2], [2], [2]],
-    'pred_aspect_ratios': [[[1], [1.05, 0.62]],
-                           [[1.29, 0.79, 0.47, 2.33, 0.27]],
-                           [[1.19, 0.72, 0.43, 2.13, 0.25]],
-                           [[1.34, 0.84, 0.52, 2.38, 0.30]],
-                           [[1.40, 0.95, 0.64, 2.16]]],
+    'selected_layers': list(range(2, 8)),
+    'pred_scales': [[1]]*6,
+    'pred_aspect_ratios': [ [[0.66685089, 1.7073535, 0.87508774, 1.16524493, 0.49059086]] ] * 6,
 })
 
 resnet50_backbone = resnet101_backbone.copy({
@@ -143,11 +139,10 @@ def replace(self, new_config_dict):
     'name': 'yolact_resnet101',
     'backbone': resnet101_backbone,
 
-    'min_size': 400,
-    'max_size': 600,
+    'max_size': 550,
 
     'train_masks': True,
-    'preserve_aspect_ratio': True,
+    'preserve_aspect_ratio': False,
     'use_prediction_module': True,
     'use_yolo_regressors': True,
 })
@@ -161,7 +156,7 @@ def replace(self, new_config_dict):
         'pred_aspect_ratios': [ [[1], [1, sqrt(2), 1/sqrt(2), sqrt(3), 1/sqrt(3)][:n]] for n in [3, 5, 5, 5, 3, 3] ],
     }),
 
-    'max_size': 600,
+    'max_size': 550,
 
     'train_masks': True,
     'preserve_aspect_ratio': False,
diff --git a/eval.py b/eval.py
index 639127463..aa71ba2c8 100644
--- a/eval.py
+++ b/eval.py
@@ -7,7 +7,7 @@
 from utils.functions import sanitize_coordinates, SavePath
 import pycocotools
 
-from data import cfg
+from data import cfg, set_cfg
 
 import numpy as np
 import torch
@@ -73,11 +73,16 @@ def str2bool(v):
                     help='The output file for coco mask results if --coco_results is set.')
 parser.add_argument('--max_num_detections', default=100, type=int,
                     help='The maximum number of detections to consider for each image for mAP scoring. COCO uses 100.')
+parser.add_argument('--config', default=None,
+                    help='The config object to use.')
 
 parser.set_defaults(display=False, resume=False, output_coco_json=False, shuffle=False)
 
 args = parser.parse_args()
 
+if args.config is not None:
+    set_cfg(args.config)
+
 iou_thresholds = [x / 100 for x in range(50, 100, 5)]
 coco_cats = [] # Call prep_coco_cats to fill this
 
@@ -577,6 +582,7 @@ def evaluate(net, dataset):
                 if it > 1:
                     print('Avg FPS: %.4f' % (1 / frame_times.get_avg()))
                 plt.imshow(np.clip(img_numpy, 0, 1))
+                plt.title(str(dataset.ids[image_idx]))
                 plt.show()
             else:
                 if it > 1: fps = 1 / frame_times.get_avg()
diff --git a/yolact.py b/yolact.py
index 629ad93ab..14c9af753 100644
--- a/yolact.py
+++ b/yolact.py
@@ -118,7 +118,7 @@ def make_priors(self, conv_h, conv_w):
                     # +0.5 because priors are in center-size notation
                     x = (i + 0.5) / conv_w
                     y = (j + 0.5) / conv_h
-
+                    
                     for scale, ars in zip(self.scales, self.aspect_ratios):
                         for ar in ars:
                             w = scale * ar / conv_w
@@ -229,8 +229,11 @@ def forward(self, x):
     # torch.set_default_tensor_type('torch.cuda.FloatTensor')
 
     x = torch.zeros((1, 3, cfg.max_size, cfg.max_size))
-
     y = net(x)
+
+    for p in net.prediction_layers:
+        print(p.last_conv_size)
+
     print()
     for a in y:
         print(a.size(), torch.sum(a))