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https://github.com/prmelehan/swifty-ml
Converts Keras models to Core ML models for use on iOS
https://github.com/prmelehan/swifty-ml
converter coremltools keras ml python3 swift-3
Last synced: 25 days ago
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Converts Keras models to Core ML models for use on iOS
- Host: GitHub
- URL: https://github.com/prmelehan/swifty-ml
- Owner: prmelehan
- License: mit
- Created: 2017-11-26T21:14:06.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2017-12-31T16:16:39.000Z (almost 7 years ago)
- Last Synced: 2024-10-12T14:23:27.636Z (25 days ago)
- Topics: converter, coremltools, keras, ml, python3, swift-3
- Language: Python
- Size: 13.7 KB
- Stars: 7
- Watchers: 4
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Swifty-ML
### Train and convert Keras models to Core ML models easily
## Features
* Train a CNN with only images
* Convert pre-trained keras models to Core ML models
* Train and Convert a CNN into a Core ML Model
* Edit attributes of existing Core ML Models
## Why does this exist?
Swifty-ML allows developers with no experience in ML to create classifiers and improve their apps. It's also good for playing around!
## Requirements
* [Keras](https://keras.io/#installation)
* [coremltools](https://github.com/apple/coremltools#installation)
* Python 3.6
## Usage
To train a model using Swifty-ML, run the following command in terminal.
```
python modeler.py train path/to/data inceptionv3
```
This usually takes a very long time, even with a good laptop, but once finished, there will be two files. A `Core ML Model` and a `Keras .h5` model. Saved in `exported_models/coreml` and `exported_models/keras` respectively. The system also saves the state of the model during training after each epoch.
To edit a coreml model...
```
python modeler.py edit path/to/coreml/model.mlmodel
```
To convert a Keras model (.h5) to CoreML format
```
python modeler.py convert-pre-trained path/to/keras/model.h5
```
## Data Directory Format
The format for training and validation images must be the following
```
.
├── training
│ ├── class1
│ ├── class2
│ └── class3
└── validation
├── class1
├── class2
└── class3
```
If I was training a network on images of the different flags, I would have a structure like so...
```
.
├── training
│ ├── belgium
│ ├── canada
│ └── unites_states
└── validation
├── belgium
├── canada
└── unites_states
```
Note: `training` and `validation` cannot change, but the sub folders' names can, and will be the class labels for the images inside them.
## Installing Core ML Tools on Python 3.6
As of now, Core ML Tools does not provide support for Python 3.6 via `pip`. Instead, you will need to download the latest version from their GitHub and install it manually.
https://github.com/apple/coremltools
After downloading the project, `cd` into the project and run
```
python setup.py install
```
## Supported models
As of now, only a few model architectures are supported. In the future, all models supported by both `coremltools` and `keras` will be supported.
Future models include
- [ ] Xception
- [x] VGG16
- [x] VGG19
- [ ] ResNet50
- [x] InceptionV3
- [ ] InceptionResNetV2
- [x] MobileNet