plugin: model: darknet: Create YOLO Model

[johnandersen777]

DFFML is hoping to participate in Google Summer of Code (GSoC) under the Python Software Foundation umbrella. You can read all about what this means at http://python-gsoc.org/. This issue, and any others tagged gsoc and project are not generally available bugs, but related to project ideas for GSoC.

Project Idea: YOLO/darknet Model.

Project description:
DFFML's initial release included a Model for Tensorflows DNN estimator.

YOLOv1,2,3 are awesome, it would be possible to wrap the YOLO work in a DFFML model and then it could be used within the DFFML API.

This involves filling out the Model abstract base class, just as the Tensorflow DNN does (use this as an example, or probably more as an education because this is likely to be rather different as we're working with images).

Tensorflow DNN: https://github.com/intel/dffml/blob/master/model/tensorflow/dffml_model_tensorflow/model/dnn.py

Model ABC:

dffml/dffml/model/model.py

Lines 30 to 52 in dd8007d

	async def train(self, sources: Sources, features: Features,
	classifications: List[Any], steps: int, num_epochs: int):
	'''
	Train using repos as the data to learn from.
	'''
	raise NotImplementedError()
	@abc.abstractmethod
	async def accuracy(self, sources: Sources, features: Features,
	classifications: List[Any]) -> Accuracy:
	'''
	Evaluates the accuracy of our model after training using the input repos
	as test data.
	'''
	raise NotImplementedError()
	@abc.abstractmethod
	async def predict(self, repos: AsyncIterator[Repo], features: Features,
	classifications: List[Any]) -> \
	AsyncIterator[Tuple[Repo, Any, float]]:
	'''
	Uses trained data to make a prediction about the quality of a repo.
	'''

Skills: Python, git
Difficulty level: Hard

Related Readings/Links:

• https://pjreddie.com/darknet/yolo/
• https://github.com/pjreddie/darknet

Demo video

Potential mentors: @pdxjohnny

Getting Started: Start by copying the directory model/tensorflow to model/darknet and re-naming everything. Then move dnn.py to darknet.py (still re-naming everything) and make sure all the tests in the model/darknet directory still pass. Then you'll need to gut the DNN class and start replacing it withsubprocess.call or check_output or whatever which will call out the the darknet binary which you compiled from Joseph's darknet repo for training, accuracy, and prediction (which is object detection in this case).

What we want to see in your application: Describe how you intend to solve the problem, and give us some "stretch goals", perhaps use the Python C bindings instead of calling subprocess out to the darknet binary. Don't forget to include some time for building appropriate tests.

[hsfzxjy]

@pdxjohnny Hi. I'm Jingyi Xie from China, an undergraduate from University of Science and Technology of China. I've used Python for many years to accomplish various jobs, including some deep learning tasks. I would like to participate in GSOC and contribute to this challenging issue. Now I'm wondering what should I do for my first commit? It'll be appreciated if you could give some advice. Thanks!

[johnandersen777]

Hi @hsfzxjy ! Thanks for your interest in DFFML! I'd say the best place for you to start is with the new model tutorial.

You could implement a Model class using an ML library you're familiar with, or do it without a library if you'd rather.

[hsfzxjy]

OK. I will try to get it ready in one day or two.

[hsfzxjy]

@pdxjohnny I've created a simple demo to classify IRIS dataset using pytorch. Here is the PR: #42

[hsfzxjy]

Hi @pdxjohnny . I've just finished my GSOC application and sent it to your gmail. Would you please have a look and give some advice? Thanks!