From 25d4c9d9aee48e4e33f59ab5a73d18ae98679aea Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ignacio=20L=C3=B3pez-Francos?= Date: Sun, 7 Jul 2019 11:50:24 -0700 Subject: [PATCH] fixing small typos --- tensorflow-planespotting/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow-planespotting/README.md b/tensorflow-planespotting/README.md index 06863e2..1234cd8 100644 --- a/tensorflow-planespotting/README.md +++ b/tensorflow-planespotting/README.md @@ -29,7 +29,7 @@ needed to provide detection capabilities. They can be found in file A script is provided for running the training on AI Platform. It implements auto-incrementing job names so that successive trainings are called job001, job002 and so on. -Apart from that, it is contains little more that the `gcloud ml-engine jobs submit training`. +Apart from that, it contains little more than the `gcloud ml-engine jobs submit training`. The script is [cloudrun_yolo.bash](cloudrun_yolo.bash). By default, it trains a 17 layer squeezenet/YOLO detection model. To start training, fill out the prerequisites and run the script. @@ -95,7 +95,7 @@ own bucket for output data: in TFRecord format. The following command line parameter switches between the two modes. Adjust it in the [cloudrun_yolo.bash](cloudrun_yolo.bash) file: ```bash - # To train train from large aerial photographs use: + # To train from large aerial photographs use: --data gs://planespotting-data-public/USGS_public_domain_photos # Alternatively, to train from 256x256 tiles in TFRecord format use: --tiledata gs://planespotting-data-public/tiles_from_USGS_photos