Rekognition Custom Model Training with true negative images

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I am trying to train the custom labels model using a dataset. Since the bounding boxes are mandatory to train the model, i need to remove some of the true negative images which does not have any labels. Due to this I believe that the performance of the model is degraded a little hampering to reduce the precision. I want to understand how can i add the true negative images without any labels for the training of the model. Also correct me if my understanding is not correct with the model training.

질문됨 4달 전146회 조회
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Certainly! To improve your Amazon Rekognition Custom Labels model:

  • Define the attributes you want to identify.
  • Collect relevant data.
  • Add negative labels for better accuracy.

You’re on the right track!

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답변함 4달 전
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  • When you mention negative labels, what should i exactly do here. I do not understand the last bullet point. Thanks for the help

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This blog post covers negative labels with Rekognition.
https://aws.amazon.com/blogs/machine-learning/tips-to-improve-your-amazon-rekognition-custom-labels-model/

Hope this helps!

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답변함 4달 전
  • Thanks for the response. I have gone through the link you shared. I still have doubt regarding this. I am using a bounding box for detecting an object. When you say the negative label then for such an image what bounding box shall i provide? This creates another scenario for me. If i provide the classification label instead of bounding box detection then do i also need to provide the true class classification label for training the custom model?

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