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Update README.md

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Glenn Jocher 2019-03-20 13:35:39 +02:00 committed by GitHub
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@ -45,21 +45,6 @@ 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.
@ -76,6 +61,21 @@ HS**V** Intensity | +/- 50%
<img src="https://user-images.githubusercontent.com/26833433/50525037-6cbcbc00-0ad9-11e9-8c38-9fd51af530e0.jpg"> <img src="https://user-images.githubusercontent.com/26833433/50525037-6cbcbc00-0ad9-11e9-8c38-9fd51af530e0.jpg">
## 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
# Inference # Inference
Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder: Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder: