https://github.com/nbortolotti/tensorphotoxray
https://github.com/nbortolotti/tensorphotoxray
appengine-python python tensorflow tensorflow-tutorials
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/nbortolotti/tensorphotoxray
- Owner: nbortolotti
- Created: 2017-08-04T06:32:18.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2018-09-12T18:51:32.000Z (almost 8 years ago)
- Last Synced: 2025-03-31T13:04:22.962Z (over 1 year ago)
- Topics: appengine-python, python, tensorflow, tensorflow-tutorials
- Language: Python
- Size: 11.7 KB
- Stars: 9
- Watchers: 3
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# AppEngine Flexible Environment for TensorFlow Photo Analysis
To use this demonstration, the TensorFlow Object Detection API should be used. For more details [here](https://github.com/tensorflow/models/tree/master/object_detection).
To use this example you will need these minimum elements of [TensorFlow Object Detection API](https://github.com/tensorflow/models/tree/master/object_detection):
Object model, quick option to automatize the deploy:
```
git clone https://github.com/tensorflow/models.git
```
*validate dependency with protoc tool
```
protoc ./models/research/object_detection/protos/string_int_label_map.proto --python_out=.
```
```
cp -R models/research/object_detection/ object_detection/
```
```
rm -rf model
```
You can use the preferred model: faster_rcnn_inception_resnet_v2_atrous_coco_2017_11_08 or as another like faster_rcnn_inception_v2_coco_2017_11_08
# Deploy the project
Note: You should have the [Google Cloud SDK](https://cloud.google.com/sdk/docs/). More information about App Engine Flexible environment, Python [here](https://cloud.google.com/appengine/docs/flexible/python/quickstart)
Local:
python main.py
Production Environment:
gcloud app deploy
*(-v version) if you want to deploy it to a specific version.