diff --git a/README.md b/README.md
index cc8ce866..87ac3554 100755
--- a/README.md
+++ b/README.md
@@ -138,54 +138,37 @@ Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
 # mAP
 
 - `test.py --weights weights/yolov3.weights` tests official YOLOv3 weights.
-- `test.py --weights weights/last.pt` tests most recent checkpoint.
-- `test.py --weights weights/best.pt` tests best checkpoint.
+- `test.py --weights weights/last.pt` tests latest checkpoint.
 - Compare to darknet published results https://arxiv.org/abs/1804.02767.
 
-[ultralytics/yolov3](https://github.com/ultralytics/yolov3) mAP@0.5 ([darknet](https://arxiv.org/abs/1804.02767)-reported mAP@0.5)
+[ultralytics/yolov3](https://github.com/ultralytics/yolov3) mAP@0.5 vs. [darknet](https://arxiv.org/abs/1804.02767)-reported mAP@0.5
 
-<i></i>         | 320         | 416         | 608
----             | ---         | ---         | ---
-`YOLOv3`        | 51.8 (51.5) | 55.4 (55.3) | 58.2 (57.9)
-`YOLOv3-SPP`    | 53.7        | 57.7        | 60.7 (60.6)
-`YOLOv3-tiny`   | 29.0        | 32.9 (33.1) | 35.5
+<i></i>                    | 320         | 416         | 608
+---                        | ---         | ---         | ---
+darknet `YOLOv3-tiny`      | 29.0        | 33.1        | 35.5
+darknet `YOLOv3`           | 51.5        | 55.3        | 57.9
+darknet `YOLOv3-SPP`       | 52.3        | 56.8        | **60.6**
+ultralytics `YOLOv3-SPP`   | **53.9**    | **58.7**    | 60.1
 
 ```bash
-$ python3 test.py --save-json --img-size 608
-Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
+$ python3 test.py --save-json --img-size 608 --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=0.5, save_json=True, weights='ultralytics68.pt')
 Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
-                Class    Images   Targets         P         R       mAP        F1: 100% 313/313 [07:40<00:00,  2.34s/it]
-                  all     5e+03  3.58e+04     0.119     0.788     0.594     0.201
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.367 <---
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.607 <---
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.387
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.208
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.392
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.297
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.465
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.495
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.332
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.518
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621
 
-$ python3 test.py --save-json --img-size 416
-Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3s-ultralytics.pt')
-Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
-                Class    Images   Targets         P         R       mAP        F1: 100% 313/313 [07:01<00:00,  1.41s/it]
-                  all     5e+03  3.58e+04      0.11     0.739     0.569     0.185
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.373
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.577
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.392
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.175
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.403
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.537
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.313
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.482
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.501
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.266
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.693
+               Class    Images   Targets         P         R   mAP@0.5        F1: 100% 313/313 [06:52<00:00,  1.24it/s]
+                 all     5e+03  3.58e+04     0.107     0.779      0.59     0.182
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.398 <---
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.601 <---
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.425
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.438
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.505
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.325
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.519
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.543
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.366
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.584
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.665
 ```
 
 # Citation