diff --git a/test.py b/test.py
index f31dadd9..5521b246 100644
--- a/test.py
+++ b/test.py
@@ -8,7 +8,7 @@ parser = argparse.ArgumentParser()
 parser.add_argument('-batch_size', type=int, default=32, help='size of each image batch')
 parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='path to model config file')
 parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='path to data config file')
-parser.add_argument('-weights_path', type=str, default='checkpoints/yolov3.pt', help='path to weights file')
+parser.add_argument('-weights_path', type=str, default='checkpoints/yolov3.weights', help='path to weights file')
 parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file')
 parser.add_argument('-iou_thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
 parser.add_argument('-conf_thres', type=float, default=0.5, help='object confidence threshold')
@@ -117,11 +117,10 @@ for batch_i, (imgs, targets) in enumerate(dataloader):
         # Compute mean AP for this image
         mAP = AP.mean()
 
-        # Append image mAP to list of validation mAPs
-        mAPs.append(mAP)
+        # Append image mAP to list
 
         # Print image mAP and running mean mAP
-        print('+ Sample [%d/%d] AP: %.4f (%.4f)' % (len(mAPs), len(dataloader) * opt.batch_size, AP, np.mean(mAPs)))
+        print('+ Sample [%d/%d] AP: %.4f (%.4f)' % (len(mAPs), len(dataloader) * opt.batch_size, mAP, np.mean(mAPs)))
 
 
 print('Mean Average Precision: %.4f' % np.mean(mAPs))