diff --git a/README.md b/README.md
index 90e54c80..34f02d47 100755
--- a/README.md
+++ b/README.md
@@ -147,30 +147,31 @@ Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
|Size |COCO mAP
@0.5...0.95 |COCO mAP
@0.5
--- | --- | --- | ---
-YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**YOLOv3-SPP ultralytics** |320 |14.0
28.7
30.5
**35.4** |29.0
51.5
52.3
**54.3**
-YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**YOLOv3-SPP ultralytics** |416 |16.0
31.1
33.9
**39.0** |32.9
55.3
56.8
**59.2**
-YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**YOLOv3-SPP ultralytics** |608 |16.6
33.0
37.0
**40.7** |35.5
57.9
60.6
**60.7**
+YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**YOLOv3-SPP ultralytics** |320 |14.0
28.7
30.5
**35.4** |29.1
51.8
52.3
**54.3**
+YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**YOLOv3-SPP ultralytics** |416 |16.0
31.2
33.9
**39.0** |33.0
55.4
56.9
**59.2**
+YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**YOLOv3-SPP ultralytics** |512 |16.6
32.7
35.6
**40.3** |34.9
57.7
59.5
**60.6**
+YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**YOLOv3-SPP ultralytics** |608 |16.6
33.1
37.0
**40.7** |35.4
58.2
60.7
**60.9**
```bash
$ python3 test.py --save-json --img-size 608 --nms-thres 0.7 --weights ultralytics68.pt
-Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', device='', img_size=608, iou_thres=0.5, nms_thres=0.7, save_json=True, weights='ultralytics68.pt')
-Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
+Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', device='1', img_size=608, iou_thres=0.5, nms_thres=0.7, save_json=True, weights='ultralytics68.pt')
+Using CUDA device0 _CudaDeviceProperties(name='GeForce RTX 2080 Ti', total_memory=11019MB)
-Downloading https://drive.google.com/uc?export=download&id=1Jm8kqnMdMGUUxGo8zMFZMJ0eaPwLkxSG as ultralytics68.pt... Done (2.2s)
- Class Images Targets P R mAP@0.5 F1: 100% 313/313 [16:23<00:00, 1.59s/it]
- all 5e+03 3.58e+04 0.0465 0.831 0.586 0.0868
- Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.407
- Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.598
- Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.444
- Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.245
- Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.446
- Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.511
- Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.326
- Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.537
- Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.595
- Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.427
- Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.641
- Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.706
+ Class Images Targets P R mAP@0.5 F1: 100%|███████████████████████████████████████████████████████████████████████████████████| 313/313 [09:46<00:00, 1.09it/s]
+ all 5e+03 3.58e+04 0.0481 0.829 0.589 0.0894
+
+ Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.40882
+ Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.60026
+ Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.44551
+ Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.24343
+ Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.45024
+ Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.51362
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.32644
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.53629
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.59343
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.42207
+ Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.63985
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.70688
```
# Citation