Improved Augmentations with Polygon AnnotationsĪny augmentation that changes the size, shape, or orientation of an object will benefit from the additional information that polygons provide about the shapes and locations of objects. The answer is: because it gives your model more information about the objects it can use to learn from which helps them learn more and make better predictions. Object detection models are typically much faster and more widely supported, so they remain the best and most popular choice for solving many problems.Īt first blush, this might seem confusing, why would you spend the extra time and effort annotating your images with polygons if your model is only able to predict boxes? It's recommended to use a Python virtual environment to avoid any compatibility issue with your TensorFlow version.Polygons have traditionally been used for training image segmentation models, but polygons can also improve the training of object detection models (which predict bounding boxes).
PYTHON CODE ANNOTATION MANUAL
On the other hand, if you have dozens of classes or objects that are hard to detect, you should need more manual annotations to see the benefits of the semi-supervised approach.Īfter training this initial model, export your best checkpoint to the SavedModel format and you'll be ready to use the auto annotation tool! 💾 Installation If you want to detect dogs and cats and have 2000 images in your dataset, for example, probably 200 images are enough (100 per class). It's hard to determine the number of images to label manually, once it depends on the complexity of your problem. As a semi-supervised solution, it's impossible to avoid manual annotation, but you'll need to label just a small amount of your data. To use this library you will need a pre-trained object detection model with a subsample of your dataset. If you want to know more technical details about the project, please, refer to my Medium article. This auto annotation tool is based on the idea of a semi-supervised architecture, where a model trained with a small amount of labeled data is used to produce the new labels for the rest of the dataset.Īs simple as that, the library uses an initial and simplified object detection model to generate the XML files with the image annotations (considering the PASCAL VOC format).īesides that, it's possible to define a confidence threshold for the detector, acting as a trade-off for the generated predictions. Auto Annotation Tool for TensorFlow Object DetectionĪre you tired to label your images by hand when working with object detection? Have hundreds or thousands of images to label? Then this project will make your life easier, just create some annotations and let the machine do the rest for you! Contents
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