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https://github.com/wolny/keras-image-net
Simple REST app for image classification using Keras pre-trained deep learning models
https://github.com/wolny/keras-image-net
flask image-classification keras-neural-networks keras-tensorflow tensorflow
Last synced: 1 day ago
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Simple REST app for image classification using Keras pre-trained deep learning models
- Host: GitHub
- URL: https://github.com/wolny/keras-image-net
- Owner: wolny
- License: mit
- Created: 2017-07-05T17:28:19.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2017-07-06T10:34:01.000Z (over 7 years ago)
- Last Synced: 2025-01-13T01:53:41.251Z (9 days ago)
- Topics: flask, image-classification, keras-neural-networks, keras-tensorflow, tensorflow
- Language: Python
- Size: 51.8 KB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# keras-image-net
Simple REST app for image classification using [Keras pre-trained deep learning models](https://keras.io/applications/).
By default the app uses [pre-trained ResNet-50 model](https://github.com/KaimingHe/deep-residual-networks) described in the paper "Deep Residual Learning for Image Recognition"
```
@article{He2015,
author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
title = {Deep Residual Learning for Image Recognition},
journal = {arXiv preprint arXiv:1512.03385},
year = {2015}
}
```
## Run (Docker)
```
docker build -t keras-image-net .
docker run -p 5000:5000 keras-image-net
# wait for models to be loaded
# test with: curl -v http://localhost:5000/detect?imageUrl=, e.g.
curl -v http://localhost:5000/detect?imageUrl=http://expressioncoffins.com.au/wp-content/uploads/2012/06/RED-TRACTOR1.jpg
```
You should get the following response:
```
{
"imageUrl": "http://expressioncoffins.com.au/wp-content/uploads/2012/06/RED-TRACTOR1.jpg",
"results": [
{
"score": 0.6732403039932251,
"label": "tractor"
},
{
"score": 0.3161275088787079,
"label": "plow"
},
{
"score": 0.010076208040118217,
"label": "harvester"
},
{
"score": 0.00041331720422022045,
"label": "thresher"
},
{
"score": 0.00006622869841521606,
"label": "hay"
}]
}
```
## Run (command line)
Make sure you have Keras installed, then just
```
python src/res-net-50.py
```
You should see:
```
('Predicted:', [(u'n02106662', u'German_shepherd', 0.99324906), (u'n02096051', u'Airedale', 0.0033434019), (u'n02105162', u'malinois', 0.0015451796), (u'n03803284', u'muzzle', 0.0005292811), (u'n02091635', u'otterhound', 0.00021379442)])
```
## Run (GCP)
```
docker build -t eu.gcr.io/$PROJECT_NAME/keras-res-net .
gcloud docker -- push eu.gcr.io/$PROJECT_NAME/keras-res-net
kubectl run keras-res-net --image=eu.gcr.io/$PROJECT_NAME/keras-res-net --port=5000 --replicas=2
kubectl expose deployment keras-res-net --type=LoadBalancer --name=keras-res-net
```