Building accurate computer vision models to detect objects in images requires deep knowledge of each step in the process—from labeling, processing, and preparing the training and validation data, to making the right model choice and tuning the model’s hyperparameters adequately to achieve the maximum accuracy. Fortunately, these complex steps are simplified by Amazon Rekognition Custom Labels, a service of Amazon Rekognition that enables you to build your own custom computer vision models for image classification and object detection tasks without requiring any prior computer vision expertise or advanced programming skills.
In this post, we showcase how we can train a model to detect bees in images using Amazon Rekognition Custom Labels. We also compare these results against a custom-trained TensorFlow model (DIY model). We use Amazon SageMaker as the platform to develop and train our model. Finally, we demonstrate how to build a serverless architecture to process new images using

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