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@ -45,6 +45,21 @@ Each epoch trains on 120,000 images from the train and validate COCO sets, and t
`from utils import utils; utils.plot_results()` `from utils import utils; utils.plot_results()`
![Alt](https://user-images.githubusercontent.com/26833433/53494085-3251aa00-3a9d-11e9-8af7-8c08cf40d70b.png "train.py results") ![Alt](https://user-images.githubusercontent.com/26833433/53494085-3251aa00-3a9d-11e9-8af7-8c08cf40d70b.png "train.py results")
# Speed
https://cloud.google.com/deep-learning-vm/
**Machine type:** n1-highmem-4 (4 vCPUs, 26 GB memory)
**CPU platform:** Intel Skylake
**GPUs:** 1-4 x NVIDIA Tesla P100
**HDD:** 100 GB SSD
GPUs | `batch_size` | speed | COCO epoch
--- |---| --- | ---
(P100) | (images) | (s/batch) | (min/epoch)
1 | 24 | 0.84s | 70min
2 | 48 | 1.27s | 53min
4 | 96 | 2.11s | 44min
## Image Augmentation ## Image Augmentation
`datasets.py` applies random OpenCV-powered (https://opencv.org/) 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. `datasets.py` applies random OpenCV-powered (https://opencv.org/) 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.