LandingLens | LandingLens on Snowflake |
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Root Cause | Troubleshooting Tips |
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Images in the dataset are mislabeled. | Check if images are labeled correctly, and fix any mislabels. Add details to the Label Book to help eliminate confusion and get consistent labeling. |
Model needs more data. | Add and label more images of the classes that the model predicted incorrectly. |
There are no visual/distinguishable differences between two classes. | Consider merging the classes, if that makes sense for your use case. For example, if you currently have Water Spot and Oil Spot classes, consider using a class called Defect instead. |
Root Cause | Troubleshooting Tips |
---|---|
Images in the dataset are mislabeled. | Check if images are labeled correctly, and fix any mislabels. Add details to the Label Book to help eliminate confusion and get consistent labeling. |
Model needs more data. | Add and label more images of the classes that the model didn’t predict. |
Environmental noise causes labeled regions to blend into non-labeled regions. The model can’t tell the difference between the two. | Improve the environmental and lighting conditions. For tips, go to Image Capture Best Practices. If you’re using Custom Training, consider adding or increasing the strength of augmentations that mimic the real-world image capture conditions. |
Object is too small or not visible after resizing. | Increase the image size or improve the image resolution. |
Applicable to Custom Training: a data augmentation is too strong, and the object to identify is no longer visible. | If you’re using Custom Training, review the augmentation settings. Consider if any could “hide” the object; if so, you can remove that augmentation or decrease its strength. |
The confidence threshold is too high. | Use a lower confidence threshold if that makes sense for your use case. |
Root Cause | Troubleshooting Tips |
---|---|
Images in the dataset are mislabeled. | Check if images are labeled correctly, and fix any mislabels. Add details to the Label Book to help eliminate confusion and get consistent labeling. |
Model needs more data. | Add and label more images of the classes that the model predicted incorrectly. If you have multiple classes, try to have the same number of examples of each class. |
Environmental noise causes labeled regions to blend into non-labeled regions. The model can’t tell the difference between the two. | Improve the environmental and lighting conditions. For tips, go to Image Capture Best Practices. If you’re using Custom Training, consider adding or increasing the strength of augmentations that mimic the real-world image capture conditions. |
The confidence threshold is too low. | Use a higher confidence threshold if that makes sense for your use case. |