Difficulty: Beginner
Estimated Time: 5 minutes


In this tutorial, you will learn how to deploy and query an object detection application called YOLO from a pre-trained TensorFlow model using the Skymind Intelligence Layer (SKIL). Let's go!

SKIL Conceptual Diagram

You did it. Great Job!

You just ran and deployed an advanced object detection application. All you needed to do was:

  • Download the model
  • Define a SKIL workspace and experiment
  • Register your model in SKIL and deploy it as a service
  • Use the SKIL service method annotate_image to find objects in an input image

Request a Demo

Wanna learn more about how SKIL can make your life easier? Contact us to request a demo!

Get Updates About Future Tutorials

Deploy an Object Detection App With SKIL

Step 1 of 4

Step 1

Downloading a pretrained object detection model

We've pretrained and uploaded an object detection model called You only look once, or YOLO, so you can download it into this tutorial and deploy it. Just run:

curl -o ./yolo_v2.pb https://github.com/deeplearning4j/dl4j-test-resources/raw/4fbca7f8286b7e0856903828193f50c08ceb1cee/src/main/resources/tf_graphs/examples/yolov2_608x608/frozen_model.pb

YOLO can detect objects in images by making bounding boxes around those objects, together with a probability assessing how likely the model thinks this box actually contains that object. The version of YOLO is trained on the so-called COCO dataset, which contains 80 real-world categories like person, dog or cat. If you take the following image, you can expect the model to find all people, cars, bikes and umbrellas in it.

YOLO input

Next, you'll see how to deploy your downloaded model quickly to get an image with labeled bounding boxes for this input image. Go ahead and download it here:

curl -o input.jpg https://raw.githubusercontent.com/SkymindIO/skil-python/cc99a0d9bb67d63f21233fad264a0fa5c1eae4c9/examples/tensorflow-yolo/input.jpg