diff --git a/README.md b/README.md
index 5d4eec39..0407e452 100755
--- a/README.md
+++ b/README.md
@@ -20,12 +20,27 @@ Python 3.6 or later with the following `pip3 install -U -r requirements.txt` pac
 # Training
 
 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 "training loss")
+![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_training_loss.png "coco training loss")
+
+## 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. Examples pictured below.
+
+- Translation:  +/- 20% X and Y
+- Rotation:     +/- 5 degrees
+- Skew:         +/- 3 degrees
+- Scale:        +/- 20%
+- Reflection:   50% probability left-right
+- Saturation:   +/- 50%
+- Intensity:    +/- 50%
+
+![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_augmentation_examples.png "coco image augmentation")
+
 
 # Inference
 
 Checkpoints will be saved in `/checkpoints` directory. Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here.
-![Alt](https://github.com/ultralytics/yolov3/blob/master/data/zidane_result.jpg "example")
+![Alt](https://github.com/ultralytics/yolov3/blob/master/data/zidane_result.jpg "inference example")
 
 # Testing