diff --git a/README.md b/README.md
index b0eff517..b10092a0 100755
--- a/README.md
+++ b/README.md
@@ -155,11 +155,12 @@ YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**[YOLOv3-SPP-ultralytics](https://drive.
 ```bash
 $ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 608
 
-Namespace(batch_size=32, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco2014.data', device='', img_size=608, iou_thres=0.6, save_json=True, single_cls=False, task='test', weights='weights/yolov3-spp-ultralytics.pt')
+Namespace(batch_size=32, cfg='yolov3-spp.cfg', conf_thres=0.001, data='data/coco2014.data', device='', img_size=608, iou_thres=0.6, save_json=True, single_cls=False, task='test', weights='weights/yolov3-spp-ultralytics.pt')
 Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
 
               Class    Images   Targets         P         R   mAP@0.5        F1: 100%|█████| 157/157 [02:46<00:00,  1.06s/it]
                  all     5e+03  3.51e+04     0.822     0.433     0.611     0.551
+
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.419
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.618
  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.448
@@ -172,6 +173,8 @@ Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memo
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.440
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.649
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.735
+
+Speed: 6.6/1.6/8.2 ms inference/NMS/total per 608x608 image at batch-size 32
 ```
 
 # Reproduce Our Results