What are the issues to be considered when preparing a data set for increasing success in model training? And by which methods to take or use photos when it is desired to create a special data set (threshold, flip, etc.) can it provide performance improvement?
Since this is an exceedingly general question which is addressed in all teaching materials for ML and data science, I’ll just get ChatGPT to give you the answer.
There are several key issues to consider when preparing a dataset for image processing to increase model training success:
- Quality and quantity of data: Ensure that the dataset is large enough and contains high quality, diverse images.
- Annotation: Label the images accurately and consistently. This is essential for training the model effectively.
- Balance: Make sure the dataset is balanced, with an equal number of examples for each class.
- Preprocessing: Preprocess the images to standardize the size, color depth, and format.
As for creating a special dataset, there are several methods you can use to augment the existing data:
- Thresholding: Binarize the images by converting them to black and white based on a threshold value.
- Flipping: Flip the images horizontally or vertically to increase the number of examples and to prevent overfitting.
- Rotations: Rotate the images by different angles to create new examples.
- Scaling: Scale the images to different sizes to create new examples.
- Blurring: Blur the images to simulate different image quality and focus levels.
It’s important to note that these methods should be used with caution, as over-augmentation can lead to decreased model performance.
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Thanks a lot for informations.