https://github.com/mayuanyang/doggy
An AI model to classify dog's actions
https://github.com/mayuanyang/doggy
keras tensorflow
Last synced: 3 months ago
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An AI model to classify dog's actions
- Host: GitHub
- URL: https://github.com/mayuanyang/doggy
- Owner: mayuanyang
- License: mit
- Created: 2017-11-18T10:45:48.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-12-20T23:19:34.000Z (over 8 years ago)
- Last Synced: 2025-01-20T11:25:58.303Z (over 1 year ago)
- Topics: keras, tensorflow
- Language: JavaScript
- Size: 83.8 MB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# doggy
An AI model to classify dog's actions, it uses Keras and Tensorflow to train and Keras-JS with vuejs as the frontend
## Setup
Follow this guide to setup the trainning and evaluation environment
### Amazon DLAMI
If you are using Amazon DLAMI, all the AI frameworks should be already installed, follow the start up screen to activate the training environment
### Your Own Environment
If you are using your own environment (the recommend way is virtualenv), follow this guide to setup Tensorflow https://www.linode.com/docs/applications/remote-desktop/install-vnc-on-ubuntu-16-04
### Libraries
We use Keras in this project, follow this guide to install it https://keras.io/#installation
We also use `pillow`, run the following command to install it
```
pip install pillow
```
## How to train
### Clone the repo
Clone this repo and download the images file
### Activate the environment
Activate the TensorFlow, depends on the environment you are in, e.g. for your local own environment, run this command `source ~/tensorflow/bin/activate`, for DLAMI, run the command for TensorFlow on the welcome screen, usually `source activate tensorflow_p36`
### Start trainning
Depends on where the images are saved, you might need to change the paths a bit inside `train.py`, change the following as required, by default the `dog_dataset` folder is the same as the `train.py`
```
# data dir
train_data_dir = 'dog_dataset/train'
validation_data_dir = 'dog_dataset/test'
```
Once the repo and images are downloaded, run this command in the TensorFlow environment `python train.py`