Topic 1 Question 126
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?
Use Cloud Vision AutoML with the existing dataset.
Use Cloud Vision AutoML, but reduce your dataset twice.
Use Cloud Vision API by providing custom labels as recognition hints.
Train your own image recognition model leveraging transfer learning techniques.
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B - You only need a PoC and it has be done quickly
👍 49Callumr2020/06/20Correct - A
👍 20[Removed]2020/03/22- 正解だと思う選択肢: A
First I think in Vision API, but that is a pre-trained AI, will not recognize my labels, so because you have 1000 samples per item, AUTO ML is perfect. B cannot be because have not sensed to reduce your dataset if you have the recommended number of info. https://cloud.google.com/vision/automl/docs/beginners-guide#include_enough_labeled_examples_in_each_category The bare minimum required by AutoML Vision training is 100 image examples per category/label. The likelihood of successfully recognizing a label goes up with the number of high quality examples for each; in general, the more labeled data you can bring to the training process, the better your model will be. Target at least 1000 examples per label.
👍 5odacir2022/12/08
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