diff --git a/README.md b/README.md
index 10318d0b..95e0555a 100755
--- a/README.md
+++ b/README.md
@@ -147,34 +147,34 @@ $ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt
|Size |COCO mAP
@0.5...0.95 |COCO mAP
@0.5
--- | --- | --- | ---
-YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |320 |14.0
28.7
30.5
**36.6** |29.1
51.8
52.3
**56.0**
-YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |416 |16.0
31.2
33.9
**40.4** |33.0
55.4
56.9
**60.2**
-YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |512 |16.6
32.7
35.6
**41.6** |34.9
57.7
59.5
**61.7**
-YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |608 |16.6
33.1
37.0
**42.1** |35.4
58.2
60.7
**61.7**
+YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |320 |14.0
28.7
30.5
**38.9** |29.1
51.8
52.3
**56.9**
+YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |416 |16.0
31.2
33.9
**42.5** |33.0
55.4
56.9
**61.1**
+YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |512 |16.6
32.7
35.6
**43.6** |34.9
57.7
59.5
**62.5**
+YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |608 |16.6
33.1
37.0
**44.0** |35.4
58.2
60.7
**62.6**
```bash
$ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 608
-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')
+Namespace(batch_size=16, 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 0.51 0.667 0.611 0.574
+ all 5e+03 3.51e+04 0.515 0.665 0.61 0.577
- 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
- Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.247
- Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.462
- Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.534
- Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.341
- Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.557
- Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.606
- 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
+ Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.434
+ Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.626
+ Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.469
+ Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263
+ Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.480
+ Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.547
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.346
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.617
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.786
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.730
+ Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.836
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.863
-Speed: 6.5/1.5/8.1 ms inference/NMS/total per 608x608 image at batch-size 32
+Speed: 6.9/2.1/9.0 ms inference/NMS/total per 608x608 image at batch-size 16
```
# Reproduce Our Results