Machine Learning by By Audrey Tam & Matthijs Hollemans & By Matthijs Hollemans & By Chris LaPollo & By Alexis Gallagher

Author:By Audrey Tam & Matthijs Hollemans & By Matthijs Hollemans & By Chris LaPollo & By Alexis Gallagher
Language: eng
Format: epub
Publisher: Ray Wenderlich

You can see that the bounding box for the “cake” annotation neatly fits around the actual slice of cake in the picture. So it looks like the data is loaded correctly!

This training image actually has three annotations. The other two are for the ice cream dessert in the top-right corner of the photo. On the right is shown the annotation from row 2. The bounding box from row 1 is very similar and covers the same object.

In the Google Open Images dataset that these images and annotations come from, often the same object in the image has multiple annotations, created by different people. That doesn’t appear to be a problem, as long as these annotations aren’t too different. After all, more training data is usually better.

However, many images have fewer annotations than there are objects, which is not ideal. For example, the image at index 3,500 in the train_annotations dataframe, with image_id 0c429e9be7f72342, has four strawberries but only three annotations, two of which are for the same strawberry. Ideally, this image would have a unique annotation for each individual object.

To get a feel for what the dataset is like, have a look at some of the other images from the training, validation and test annotations.

Because not all objects from all images have annotations, and some have duplicates, this dataset isn’t ideal — but, with over 7,000 annotations, it should still be good enough to train a decent object detection model. When you start building your own models, you’ll find that you’ll be spending a lot of time cleaning up your training data, filling in missing values, and so on. Your model will only ever be as good as the quality of the dataset, so it’s worth putting in the time.

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