diff --git a/README.md b/README.md
index e8f25e7e1b2956808bd1c8e00d124f2a119a9541..3ed0a87b7b0879fb98437be55426f53d2adca469 100755
--- a/README.md
+++ b/README.md
@@ -45,20 +45,18 @@ HS**V** Intensity | +/- 50%
 
 # Inference
 
-Run `detect.py --weights` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here. Download official YOLOv3 weights:
+Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here. Download official YOLOv3 weights:
 
 - PyTorch format: https://storage.googleapis.com/ultralytics/yolov3.pt
 - Darknet format: https://pjreddie.com/media/files/yolov3.weights
 
 ![Alt](https://github.com/ultralytics/yolov3/blob/master/data/zidane_result.jpg "inference example")
 
-# Testing
+# Validation mAP
 
-Run `test.py` to validate the official YOLOv3 weights `checkpoints/yolov3.weights` against the 5000 validation images. You should obtain a mAP of .581 using this repo (https://github.com/ultralytics/yolov3), compared to .579 as reported in darknet (https://arxiv.org/abs/1804.02767).
+Run `test.py` to validate the official YOLOv3 weights `weights/yolov3.weights` against the 5000 validation images. You should obtain a .584 mAP at `--img-size 416`, or .586 at `--img-size 608` using this repo, compared to .579 at 608 x 608 reported in darknet (https://arxiv.org/abs/1804.02767).
 
-Run `test.py --weights weights/latest.pt` to validate against the latest training 
-
-oint.
+Run `test.py --weights weights/latest.pt` to validate against the latest training results. Default training settings produce a 0.522 mAP at epoch 62. We are currently exploring how to improve this.
 
 # Contact