diff --git a/README.md b/README.md
index 0737f8c2..2079d5c1 100755
--- a/README.md
+++ b/README.md
@@ -22,7 +22,7 @@ Python 3.6 or later with the following `pip3 install -U -r requirements.txt` pac
 Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`. Each epoch trains on 120,000 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. An Nvidia GTX 1080 Ti will process ~10 epochs/day with full augmentation, or ~15 epochs/day without input image augmentation. Loss plots for the bounding boxes, objectness and class confidence should appear similar to results shown here (coming soon)
 ![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_training_loss.png "coco training loss")
 
-## Augmentation
+## Image Augmentation
 
 `datasets.py` applies random augmentation to the input images in accordance with the following specifications. Augmentation is applied *only* during training, not during inference. Bounding boxes are automatically tracked and updated with the images. 416 x 416 examples pictured below.