Acquire Data
Before you train a deep learning model, you need to acquire high-quality image data in accordance with the Data Acquisition Standard for model training.
Preparations before Acquisition
Before acquiring data, ensure the following preparations are completed:
Considerations during Acquisition
When acquiring data, it is necessary to consider various conditions of the actual application, including the following:
-
Ensure that the acquired dataset includes all possible object placement orientations in actual applications.
-
Ensure that the acquired dataset includes all possible object positions in actual applications.
-
Ensure that the acquired dataset includes all possible positional relationships between objects in actual applications.
For more information, see Considerations during Acquisition.
Selection Standards after Acquisition
After acquiring data, select the images for deep learning model training based on the following standards:
-
Control the image quality and quantity of training set.
-
Acquire representative data.
-
Balance data proportion.
-
Images should be consistent with the application site.
For more information, see Selection Standards after Acquisition.