diff --git a/README.md b/README.md
index 6e9f5913..6042076a 100755
--- a/README.md
+++ b/README.md
@@ -149,36 +149,36 @@ YOLOv3-608 | 57.9 (58.2) | 57.9
 `YOLOv3-spp 608` | 60.7 | 60.6  
 
 ``` bash
-sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
+git clone https://github.com/ultralytics/yolov3
 # bash yolov3/data/get_coco_dataset.sh
-sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
+git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
 cd yolov3
  
-python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16
-Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3.weights')
-Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
-      Image      Total          P          R        mAP
-Calculating mAP: 100%|█████████████████████████████████| 313/313 [08:54<00:00,  1.55s/it]
-       5000       5000     0.0966      0.786      0.579
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.331
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.582
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.344
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.198
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.362
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.427
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.281
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.437
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.463
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.309
+python3 test.py --save-json --img-size 416
+Namespace(batch_size=32, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
+Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
+               Class    Images   Targets         P         R       mAP        F1
+Calculating mAP: 100%|█████████████████████████████████████████| 157/157 [05:59<00:00,  1.71s/it]
+                 all     5e+03  3.58e+04     0.109     0.773      0.57     0.186
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.335
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.565
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.349
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.360
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.493
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.280
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.432
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.458
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577
- 
-python3 test.py --weights weights/yolov3-spp.weights --cfg cfg/yolov3-spp.cfg --save-json --img-size 608 --batch-size 8
-Namespace(batch_size=8, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
-Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
-       Image      Total          P          R        mAP
-Calculating mAP: 100%|█████████████████████████████████| 625/625 [07:01<00:00,  1.56it/s]
-       5000       5000       0.12       0.81      0.611
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.620
+
+python3 test.py --save-json --img-size 608 --batch-size 16
+Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
+Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
+               Class    Images   Targets         P         R       mAP        F1
+Computing mAP: 100%|█████████████████████████████████████████| 313/313 [06:11<00:00,  1.01it/s]
+                 all     5e+03  3.58e+04      0.12      0.81     0.611     0.203
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.366
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.607
  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.386
@@ -191,6 +191,7 @@ Calculating mAP: 100%|███████████████████
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.331
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.517
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618
+
 ```
 
 # Citation