YOLK: Keras Object Detection API
Every year newly developed Object Detection architectures are introduced, but even applying the simplest ones has been something with, or perhaps more than, a big hassle so far. YOLKYou Look Only Keras is an one-stop Object Detection API for Keras, which is built as a part of 2019 Open Source Contributhon. It shares the idea of what Keras is created for: Being able to go from idea to result with the least possible delay is key to doing good research. With a few lines of codes, you can set up and apply one of the best performing models to your own datasets. We hope this can help everyone train their own Object Detection models painlessly.
KerasObjectDetector ├── datasets ├── docker ├── examples │ ├── ResNet50RetinaNet │ ├── YOLOv3_Prediction │ ├── YOLOv3_Training │ ├── retinanet_inference_example │ ├── retinanet_training_example │ ├── ssd_inference_example │ └── ssd_training_example ├── keras_retinanet ├── keras_ssd ├── keras_yolov3 ├── res ├── tests ├── yolk ├── .gitignore ├── LICENSE ├── README.md ├── setup.cfg ├── setup.py
Installation (On Linux)
Download YOLK API. This API helps setting up development environment for Object Detection tasks. Run the following commands in terminal.
# Download YOLK API $ git clone https://github.com/KerasKorea/KerasObjectDetector.git $ cd KerasObjectDetector # If there is no 'setuptools' in docker, please download this package. # pip install setuptools # install library $ apt-get install libatlas-base-dev libxml2-dev libxslt-dev python-tk # build setup codes # ./KerasObjectDetector $ python setup.py install
If you want to run the API on a docker, download our pre-built docker image from Docker HUB and run.
# pull yolk docker image $ docker pull kerasyolk/yolk # run yolk $ docker run --name=yolk -Pit -p 8888:8888 -p 8022:22 kerasyolk/yolk:latest # running jupyter-notebook $ jupyter-notebook
You can test your own image with a Quick Start :)
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