We can verify that all the images are, indeed, the ones we want to include in our dataset and that our annotations are being parsed properly. For example, if some of the annotations improperly extended beyond the frame of an image, Roboflow intelligently crops the edge of the annotation to line up with the edge of the image and drops erroneous annotations that lie fully outside the image frame.Īt this point, our images have not yet been uploaded to Roboflow. If any of your annotations have errors, Roboflow alerts you. Once you drop the chess-tutorial-dataset folder into Roboflow, the images and annotations are processed for you to see them overlayed. While these are VOC XML, note Roboflow supports most every annotation format.
Click and drag the folder called “chess-tutorial-dataset” from your local machine onto the highlighted upload area.Īs an aside, feel free to poke around the contents of chess-tutorial-dataset on your computer so you can see what’s inside: We’ve provided 12 chess images and VOC XML annotations. Now, unzip the sample file we just downloaded to your computer, sampleChessDataset.zip.
We automatically take care of (1) creating and naming your dataset “Chess Sample”, (2) choosing an " Object Detection" dataset type, and (3) naming your annotations “Pieces.” You’ve also downloaded a zip file containing 20 chess images and their annotations across the 12 different pieces.Īs the guided tutorial suggests, click “ Create Dataset.” to continue. Click "Create New Project" and then "Download Sample Project", which will give you a zip file containing example images and annotations for you to use in the tutorial. To get started, create an account using your email or GitHub account: Īfter reviewing and accepting the terms of service, you’ll land on your projects homepage: The Roboflow Dashboard.įor this walkthrough, we’ll use the Roboflow-provided sample dataset. Let’s walkthrough a tutorial on managing images for a chess piece detection problem.
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