# Debugging non-terminal dataset errors
The following are non-terminal errors that can occur during dataset creation or update. These errors can invalidate an entire JSON Line or invalidate annotations within a JSON Line. If a JSON Line has an error, it is not used for training. If an annotation within a JSON Line has an error, the JSON Line is still used for training, but without the broken annotation. For more information about JSON Lines, see [Creating a manifest file](md-create-manifest-file.md).
You can access non-terminal errors from the console and by calling the `ListDatasetEntries` API. For more information, see [Listing dataset entries (SDK)](md-listing-dataset-entries-sdk.md).
The following errors are are also returned during training. We recommend that you fix these errors before training your model.For more information, see [Non-Terminal JSON Line Validation Errors](tm-debugging-json-line-errors.md).
+ [ERROR\_NO\_LABEL\_ATTRIBUTES](tm-debugging-json-line-errors.md#tm-error-ERROR_NO_LABEL_ATTRIBUTES)
+ [ERROR\_INVALID\_LABEL\_ATTRIBUTE\_FORMAT](tm-debugging-json-line-errors.md#tm-error-ERROR_INVALID_LABEL_ATTRIBUTE_FORMAT)
+ [ERROR\_INVALID\_LABEL\_ATTRIBUTE\_METADATA\_FORMAT](tm-debugging-json-line-errors.md#tm-error-ERROR_INVALID_LABEL_ATTRIBUTE_METADATA_FORMAT)
+ [ERROR\_NO\_VALID\_LABEL\_ATTRIBUTES](tm-debugging-json-line-errors.md#tm-error-ERROR_NO_VALID_LABEL_ATTRIBUTES)
+ [ERROR\_INVALID\_BOUNDING\_BOX](tm-debugging-json-line-errors.md#tm-error-ERROR_INVALID_BOUNDING_BOX)
+ [ERROR\_INVALID\_IMAGE\_DIMENSION](tm-debugging-json-line-errors.md#tm-error-ERROR_INVALID_IMAGE_DIMENSION)
+ [ERROR\_BOUNDING\_BOX\_TOO\_SMALL](tm-debugging-json-line-errors.md#tm-error-ERROR_BOUNDING_BOX_TOO_SMALL)
+ [ERROR\_NO\_VALID\_ANNOTATIONS](tm-debugging-json-line-errors.md#tm-error-ERROR_NO_VALID_ANNOTATIONS)
+ [ERROR\_MISSING\_BOUNDING\_BOX\_CONFIDENCE](tm-debugging-json-line-errors.md#tm-error-ERROR_MISSING_BOUNDING_BOX_CONFIDENCE)
+ [ERROR\_MISSING\_CLASS\_MAP\_ID](tm-debugging-json-line-errors.md#tm-error-ERROR_MISSING_CLASS_MAP_ID)
+ [ERROR\_TOO\_MANY\_BOUNDING\_BOXES](tm-debugging-json-line-errors.md#tm-error-ERROR_TOO_MANY_BOUNDING_BOXES)
+ [ERROR\_UNSUPPORTED\_USE\_CASE\_TYPE](tm-debugging-json-line-errors.md#tm-error-ERROR_UNSUPPORTED_USE_CASE_TYPE)
+ [ERROR\_INVALID\_LABEL\_NAME\_LENGTH](tm-debugging-json-line-errors.md#tm-error-ERROR_INVALID_LABEL_NAME_LENGTH)
## Accessing non-terminal errors
You can use the console to find out which images in a dataset have non-terminal errors. You can also call, call `ListDatasetEntries` API to get the error messages. For more information, see [Listing dataset entries (SDK)](md-listing-dataset-entries-sdk.md).
**To access non-terminal errors(console)**
1. Open the Amazon Rekognition console at [https://console.aws.amazon.com/rekognition/](https://console.aws.amazon.com/rekognition/).
1. Choose **Use Custom Labels**.
1. Choose **Get started**.
1. In the left navigation pane, choose **Projects**.
1. In the **Projects** page, choose the project that you want to use. The details page for your project is displayed.
1. If you want to view non-terminal errors in your training dataset, choose the **Training** tab. Otherwise choose the **Test** tab to view non-terminal errors in your test dataset.
1. In the **Labels** section of the dataset gallery, choose **Errors**. The dataset gallery is filtered to only show images with errors.
1. Choose **Error** underneath an image to see the error code. Use the information at [Non-Terminal JSON Line Validation Errors](tm-debugging-json-line-errors.md) to fix the error.
