diff --git a/README.md b/README.md
index a4d1469e..217be811 100755
--- a/README.md
+++ b/README.md
@@ -19,7 +19,11 @@ 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 (in progress to 160 epochs, will update)
+Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`.
+
+Run `train.py -resume 1` to resume training from the most recently saved checkpoint `checkpoints/latest.pt`.
+
+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 (in progress to 160 epochs, will update)
 ![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_training_loss.png "coco training loss")
 
 ## Image Augmentation
@@ -40,7 +44,7 @@ HS**V** Intensity | +/- 50%
 
 # 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.
+Checkpoints are 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 "inference example")
 
 # Testing