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Update README.md

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@ -53,10 +53,10 @@ Our Jupyter [notebook](https://colab.research.google.com/github/ultralytics/yolo
Each epoch trains on 117,263 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 0.25 s/batch on a V100 GPU (almost 50 COCO epochs/day)**.
Here we see training results from `coco_1img.data`, `coco_10img.data` and `coco_100img.data`, 3 example files available in the `data/` folder, which train and test on the first 1, 10 and 100 images of the coco2014 trainval dataset.
Here we see training results from `coco_16img.data`, `coco_64img.data`, 2 example datasets available in the `data/` folder, which train and test on the first 16 and 64 images of the COCO2014-trainval dataset.
`from utils import utils; utils.plot_results()`
![results](https://user-images.githubusercontent.com/26833433/56207787-ec9e7000-604f-11e9-94dd-e1fcc374270f.png)
![results](https://user-images.githubusercontent.com/26833433/62325526-1fa82a80-b4ac-11e9-958e-2a263bf15ab0.png)
## Image Augmentation