diff --git a/README.md b/README.md
index 9c7a1bf5..0fe97933 100755
--- a/README.md
+++ b/README.md
@@ -1,6 +1,7 @@
 <table style="width:100%">
   <tr>
-    <th><img src="https://user-images.githubusercontent.com/26833433/52743528-e6096300-2fe2-11e9-970c-5fee45769fab.jpg" width="400"></th>
+    <th>v2.2<img src="https://user-images.githubusercontent.com/26833433/52743528-e6096300-2fe2-11e9-970c-5fee45769fab.jpg" width="400"></th>
+        <th>v3.0<img src="https://user-images.githubusercontent.com/26833433/54523854-227d0580-4979-11e9-9801-26a3be239875.jpg" width="400"></th>
     <th><img src="https://storage.googleapis.com/ultralytics/logo/logoname1000.png" width="200">
   <br><br/>
   <p> <a href="https://itunes.apple.com/app/id1452689527">
@@ -9,6 +10,7 @@
   </tr>
 </table>
 
+
 # Introduction
 
 This directory contains python software and an iOS App developed by Ultralytics LLC, and **is freely available for redistribution under the GPL-3.0 license**. For more information please visit https://www.ultralytics.com.
@@ -34,9 +36,9 @@ Python 3.7 or later with the following `pip3 install -U -r requirements.txt` pac
 
 # Training
 
-**Start Training:** Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`. Training runs about 1 hour per COCO epoch on a 1080 Ti.
+**Start Training:** Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`.
 
-**Resume Training:** Run `train.py --resume` to resume training from the most recently saved checkpoint `weights/latest.pt`.
+**Resume Training:** Run `train.py --resume` resumes training from the latest checkpoint `weights/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. Default training settings produce loss plots below, with **training speed of 0.6 s/batch on a 1080 Ti (18 epochs/day)** or 0.45 s/batch on a 2080 Ti.
 
@@ -64,10 +66,10 @@ HS**V** Intensity | +/- 50%
 Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder:
 
 **YOLOv3:** `detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.pt`
-<img src="https://user-images.githubusercontent.com/26833433/50524393-b0adc200-0ad5-11e9-9335-4774a1e52374.jpg" width="800">
+<img src="https://user-images.githubusercontent.com/26833433/50524393-b0adc200-0ad5-11e9-9335-4774a1e52374.jpg" width="700">
 
 **YOLOv3-tiny:** `detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.pt`
-<img src="https://user-images.githubusercontent.com/26833433/50374155-21427380-05ea-11e9-8d24-f1a4b2bac1ad.jpg" width="800">
+<img src="https://user-images.githubusercontent.com/26833433/50374155-21427380-05ea-11e9-8d24-f1a4b2bac1ad.jpg" width="700">
 
 ## Webcam
 
@@ -75,8 +77,6 @@ Run `detect.py` with `webcam=True` to show a live webcam feed.
 
 # Pretrained Weights
 
-Download official YOLOv3 weights:
-
 **Darknet** format: 
 - https://pjreddie.com/media/files/yolov3.weights
 - https://pjreddie.com/media/files/yolov3-tiny.weights