Non-Terminal JSON Line Validation Errors - Rekognition
ERROR_MISSING_SOURCE_REFERROR_INVALID_SOURCE_REF_FORMATERROR_NO_LABEL_ATTRIBUTESERROR_INVALID_LABEL_ATTRIBUTE_FORMATERROR_INVALID_LABEL_ATTRIBUTE_METADATA_FORMATERROR_NO_VALID_LABEL_ATTRIBUTESERROR_MISSING_BOUNDING_BOX_CONFIDENCEERROR_MISSING_CLASS_MAP_IDERROR_INVALID_JSON_LINEERROR_INVALID_IMAGEERROR_INVALID_IMAGE_DIMENSIONERROR_INVALID_BOUNDING_BOXERROR_NO_VALID_ANNOTATIONSERROR_BOUNDING_BOX_TOO_SMALLERROR_TOO_MANY_BOUNDING_BOXESWARNING_UNANNOTATED_RECORDWARNING_NO_ANNOTATIONSWARNING_NO_ATTRIBUTE_ANNOTATIONSERROR_UNSUPPORTED_USE_CASE_TYPEERROR_INVALID_LABEL_NAME_LENGTH

Non-Terminal JSON Line Validation Errors

This topic lists the non-terminal JSON Line validation errors reported by Amazon Rekognition Custom Labels during training. The errors are reported in the training and testing validation manifest. For more information, see Understanding training and testing validation result manifests. You can fix a non-terminal JSON Line error by updating the JSON Line in the training or test manifest file. You can also remove the JSON Line from the manifest, but doing so might reduce the quality of your model. If there are many non-terminal validation errors, you might find it easier to recreate the manifest file. Validation errors typically occur in manually created manifest files. For more information, see Creating a manifest file. For information about fixing validation errors, see Fixing training errors. Some errors can be fixed by using the Amazon Rekognition Custom Labels console.

ERROR_MISSING_SOURCE_REF

Error message

The source-ref key is missing.

More information

The JSON Line source-ref field provides the Amazon S3 location of an image. This error occurs when the source-ref key is missing or is misspelt. This error typically occurs in manually created manifest files. For more information, see Creating a manifest file.

To fix ERROR_MISSING_SOURCE_REF

  1. Check that the source-ref key is present and is spelt correctly. A complete source-ref key and value is similar to the following. is "source-ref": "s3://bucket/path/image".

  2. Update or the source-ref key in the JSON Line. Alternatively, remove, the JSON Line from the manifest file.

You can't use the Amazon Rekognition Custom Labels console to fix this error.

ERROR_INVALID_SOURCE_REF_FORMAT

Error message

The format of the source-ref value is invalid.

More information

The source-ref key is present in the JSON Line, but the schema of the Amazon S3 path is incorrect. For example, the path is https://.... instead of S3://..... An ERROR_INVALID_SOURCE_REF_FORMAT error typically occurs in manually created manifest files. For more information, see Creating a manifest file.

To fix ERROR_INVALID_SOURCE_REF_FORMAT

  1. Check that the schema is "source-ref": "s3://bucket/path/image". For example, "source-ref": "s3://custom-labels-console-us-east-1-1111111111/images/000000242287.jpg".

  2. Update, or remove, the JSON Line in the manifest file.

You can't use the Amazon Rekognition Custom Labels console to fix this ERROR_INVALID_SOURCE_REF_FORMAT.

ERROR_NO_LABEL_ATTRIBUTES

Error message

No label attributes found.

More information

The label attribute or the label attribute -metadata key name (or both) is invalid or missing. In the following example, ERROR_NO_LABEL_ATTRIBUTES occurs whenever the bounding-box or bounding-box-metadata key (or both) is missing. For more information, see Creating a manifest file.

{
	"source-ref": "s3://custom-labels-bucket/images/IMG_1186.png",
	"bounding-box": {
		"image_size": [{
			"width": 640,
			"height": 480,
			"depth": 3
		}],
		"annotations": [{
			"class_id": 1,
			"top": 251,
			"left": 399,
			"width": 155,
			"height": 101
		}, {
			"class_id": 0,
			"top": 65,
			"left": 86,
			"width": 220,
			"height": 334
		}]
	},
	"bounding-box-metadata": {
		"objects": [{
			"confidence": 1
		}, {
			"confidence": 1
		}],
		"class-map": {
			"0": "Echo",
			"1": "Echo Dot"
		},
		"type": "groundtruth/object-detection",
		"human-annotated": "yes",
		"creation-date": "2018-10-18T22:18:13.527256",
		"job-name": "my job"
	}
}

A ERROR_NO_LABEL_ATTRIBUTES error typically occurs in a manually created manifest file. For more information, see Creating a manifest file.

To fix ERROR_NO_LABEL_ATTRIBUTES

  1. Check that label attribute identifier and label attribute identifer -metadata keys are present and that the key names are spelt correctly.

  2. Update, or remove, the JSON Line in the manifest file.

You can't use the Amazon Rekognition Custom Labels console to fix ERROR_NO_LABEL_ATTRIBUTES .

ERROR_INVALID_LABEL_ATTRIBUTE_FORMAT

Error message

The format of the label attribute {} is invalid.

More information

The schema for the label attribute key is missing or invalid. An ERROR_INVALID_LABEL_ATTRIBUTE_FORMAT error typically occurs in manually created manifest files. for more information, see Creating a manifest file.

To fix ERROR_INVALID_LABEL_ATTRIBUTE_FORMAT

  1. Check that the JSON Line section for the label attribute key is correct. In the following example object location example, the image_size and annotations objects must be correct. The label attribute key is named bounding-box.

    	"bounding-box": {
		"image_size": [{
			"width": 640,
			"height": 480,
			"depth": 3
		}],
		"annotations": [{
			"class_id": 1,
			"top": 251,
			"left": 399,
			"width": 155,
			"height": 101
		}, {
			"class_id": 0,
			"top": 65,
			"left": 86,
			"width": 220,
			"height": 334
		}]
	},

  2. Update, or remove, the JSON Line in the manifest file.

You can't use the Amazon Rekognition Custom Labels console to fix this error.

ERROR_INVALID_LABEL_ATTRIBUTE_METADATA_FORMAT

Error message

The format of the label attribute metadata is invalid.

More information

The schema for the label attribute metadata key is missing or invalid. An ERROR_INVALID_LABEL_ATTRIBUTE_METADATA_FORMAT error typically occurs in manually created manifest files. For more information, see Creating a manifest file.

To fix ERROR_INVALID_LABEL_ATTRIBUTE_FORMAT

  1. Check that the JSON Line schema for the label attribute metadata key is similar to the following example. The label attribute metadata key is named bounding-box-metadata.

    	"bounding-box-metadata": {
		"objects": [{
			"confidence": 1
		}, {
			"confidence": 1
		}],
		"class-map": {
			"0": "Echo",
			"1": "Echo Dot"
		},
		"type": "groundtruth/object-detection",
		"human-annotated": "yes",
		"creation-date": "2018-10-18T22:18:13.527256",
		"job-name": "my job"
	}

  2. Update, or remove, the JSON Line in the manifest file.

You can't use the Amazon Rekognition Custom Labels console to fix this error.

ERROR_NO_VALID_LABEL_ATTRIBUTES

Error message

No valid label attributes found.

More information

No valid label attributes were found in the JSON Line. Amazon Rekognition Custom Labels checks both the label attribute and the label attribute identifier. An ERROR_INVALID_LABEL_ATTRIBUTE_FORMAT error typically occurs in manually created manifest files. for more information, see Creating a manifest file.

If a JSON Line isn't in a supported SageMaker AI manifest format, Amazon Rekognition Custom Labels marks the JSON Line as invalid and an ERROR_NO_VALID_LABEL_ATTRIBUTES error is reported. Currently, Amazon Rekognition Custom Labels supports classification job and bounding box formats. For more information, see Creating a manifest file.

To fix ERROR_NO_VALID_LABEL_ATTRIBUTES

  1. Check that the JSON for the label attribute key and label attribute metadata is correct.

  2. Update, or remove, the JSON Line in the manifest file. For more information, see Creating a manifest file.

You can't use the Amazon Rekognition Custom Labels console to fix this error.

ERROR_MISSING_BOUNDING_BOX_CONFIDENCE

Error message

One or more bounding boxes has a missing confidence value.

More information

The confidence key is missing for one or more object location bounding boxes. The confidence key for a bounding box is in the label attribute metadata, as shown in the following example. A ERROR_MISSING_BOUNDING_BOX_CONFIDENCE error typically occurs in manually created manifest files. For more information, see Object localization in manifest files.

	"bounding-box-metadata": {
		"objects": [{
			"confidence": 1
		}, {
			"confidence": 1
		}],
To fix ERROR_MISSING_BOUNDING_BOX_CONFIDENCE

  1. Check that the objects array in the label attribute contains the same number of confidence keys as there are objects in the label attribute annotations array.

  2. Update, or remove, the JSON Line in the manifest file.

You can't use the Amazon Rekognition Custom Labels console to fix this error.

ERROR_MISSING_CLASS_MAP_ID

Error message

One of more class ids is missing from the class map.

More information

The class_id in an annotation (bounding box) object doesn't have a matching entry in the label attribute metadata class map (class-map). For more information, see Object localization in manifest files. A ERROR_MISSING_CLASS_MAP_ID error typically occurs in manually created manifest files.

To fix ERROR_MISSING_CLASS_MAP_ID

  1. Check that the class_id value in each annotation (bounding box) object has a corresponding value in the class-map array, as shown in the following example. The annotations array and class_map array should have the same number of elements.

    {
	"source-ref": "s3://custom-labels-bucket/images/IMG_1186.png",
	"bounding-box": {
		"image_size": [{
			"width": 640,
			"height": 480,
			"depth": 3
		}],
		"annotations": [{
			"class_id": 1, 
			"top": 251,
			"left": 399,
			"width": 155,
			"height": 101
		}, {
			"class_id": 0,
			"top": 65,
			"left": 86,
			"width": 220,
			"height": 334
		}]
	},
	"bounding-box-metadata": {
		"objects": [{
			"confidence": 1
		}, {
			"confidence": 1
		}],
		"class-map": {
			"0": "Echo",
			"1": "Echo Dot"
		}, 
		"type": "groundtruth/object-detection",
		"human-annotated": "yes",
		"creation-date": "2018-10-18T22:18:13.527256",
		"job-name": "my job"
	}
}

  2. Update, or remove, the JSON Line in the manifest file.

You can't use the Amazon Rekognition Custom Labels console to fix this error.

ERROR_INVALID_JSON_LINE

Error message

The JSON Line has an invalid format.

More information

An unexpected character was found in the JSON Line. The JSON Line is replaced with a new JSON Line that contains only the error information. An ERROR_INVALID_JSON_LINE error typically occurs in manually created manifest files. For more information, see Object localization in manifest files.

You can't use the Amazon Rekognition Custom Labels console to fix this error.

To fix ERROR_INVALID_JSON_LINE

  1. Open the manifest file and navigate to the JSON Line where the ERROR_INVALID_JSON_LINE error occurs.

  2. Check that the JSON Line doesn't contain invalid characters and that required ; or , characters are not missing.

  3. Update, or remove, the JSON Line in the manifest file.

ERROR_INVALID_IMAGE

Error message

The image is invalid. Check S3 path and/or image properties.

More information

The file referenced by source-ref is not a valid image. Potential causes include the image aspect ratio, the size of the image, and the image format.

For more information, see Guidelines and quotas in Amazon Rekognition Custom Labels.

To fix ERROR_INVALID_IMAGE

  1. Check the following.

    • The aspect ratio of the image is less than 20:1.

    • The size of the image is greater than 15 MB

    • The image is in PNG or JPEG format.

    • The path to the image in source-ref is correct.

    • The minimum image dimension of the image is greater 64 pixels x 64 pixels.

    • The maximum image dimension of the image is less than 4096 pixels x 4096 pixels.

  2. Update, or remove, the JSON Line in the manifest file.

You can't use the Amazon Rekognition Custom Labels console to fix this error.

ERROR_INVALID_IMAGE_DIMENSION

Error message

The image dimension(s) do not conform to allowed dimensions.

More information

The image referenced by source-ref doesn't conform to the allowed image dimensions. The minimum dimension is 64 pixels. The maximum dimension is 4096 pixels. ERROR_INVALID_IMAGE_DIMENSION is reported for images with bounding boxes.

For more information, see Guidelines and quotas in Amazon Rekognition Custom Labels.

To fix ERROR_INVALID_IMAGE_DIMENSION (Console)

  1. Update the image in the Amazon S3 bucket with dimensions that Amazon Rekognition Custom Labels can process.

  2. In the Amazon Rekognition Custom Labels console, do the following:

    1. Remove the existing bounding boxes from the image.

    2. Re-add the bounding boxes to the image.

    3. Save your changes.

    For more information, Labeling objects with bounding boxes.

To fix ERROR_INVALID_IMAGE_DIMENSION (SDK)

  1. Update the image in the Amazon S3 bucket with dimensions that Amazon Rekognition Custom Labels can process.

  2. Get the existing JSON Line for the image by calling ListDatasetEntries. For the SourceRefContains input parameter specify the Amazon S3 location and filename of the image.

  3. Call UpdateDatasetEntries and provide the JSON line for the image. Make sure the value of source-ref matches the image location in the Amazon S3 bucket. Update the bounding box annotations to match the bounding box dimensions needed for the updated image.

    {
	"source-ref": "s3://custom-labels-bucket/images/IMG_1186.png",
	"bounding-box": {
		"image_size": [{
			"width": 640,
			"height": 480,
			"depth": 3
		}],
		"annotations": [{
			"class_id": 1,
			"top": 251,
			"left": 399,
			"width": 155,
			"height": 101
		}, {
			"class_id": 0,
			"top": 65,
			"left": 86,
			"width": 220,
			"height": 334
		}]
	},
	"bounding-box-metadata": {
		"objects": [{
			"confidence": 1
		}, {
			"confidence": 1
		}],
		"class-map": {
			"0": "Echo",
			"1": "Echo Dot"
		},
		"type": "groundtruth/object-detection",
		"human-annotated": "yes",
		"creation-date": "2013-11-18T02:53:27",
		"job-name": "my job"
	}
}

ERROR_INVALID_BOUNDING_BOX

Error message

The bounding box has off frame values.

More information

The bounding box information specifies an image that is either off the image frame or contains negative values.

For more information, see Guidelines and quotas in Amazon Rekognition Custom Labels.

To fix ERROR_INVALID_BOUNDING_BOX

  1. Check the values of the bounding boxes in the annotations array. 

    	"bounding-box": {
		"image_size": [{
			"width": 640,
			"height": 480,
			"depth": 3
		}],
		"annotations": [{
			"class_id": 1,
			"top": 251,
			"left": 399,
			"width": 155,
			"height": 101
		}]
	},

  2. Update, or alternatively remove, the JSON Line from the manifest file.

You can't use the Amazon Rekognition Custom Labels console to fix this error.

ERROR_NO_VALID_ANNOTATIONS

Error message

No valid annotations found.

More information

None of the annotation objects in the JSON Line contain valid bounding box information.

To fix ERROR_NO_VALID_ANNOTATIONS

  1. Update the annotations array to include valid bounding box objects. Also, check that corresponding bounding box information (confidence and class_map) in the label attribute metadata is correct. For more information, see Object localization in manifest files.

    {
	"source-ref": "s3://custom-labels-bucket/images/IMG_1186.png",
	"bounding-box": {
		"image_size": [{
			"width": 640,
			"height": 480,
			"depth": 3
		}],
		"annotations": [
		   {              
			"class_id": 1,    #annotation object
			"top": 251,
			"left": 399,
			"width": 155,
			"height": 101
		}, {
			"class_id": 0,
			"top": 65,
			"left": 86,
			"width": 220,
			"height": 334
		}]
	},
	"bounding-box-metadata": {
		"objects": [
		>{                
			"confidence": 1          #confidence  object
		}, 
        {
			"confidence": 1
		}],
		"class-map": {  
			"0": "Echo",    #label 
			"1": "Echo Dot"
		},
		"type": "groundtruth/object-detection",
		"human-annotated": "yes",
		"creation-date": "2018-10-18T22:18:13.527256",
		"job-name": "my job"
	}
}

  2. Update, or alternatively remove, the JSON Line from the manifest file.

You can't use the Amazon Rekognition Custom Labels console to fix this error.

ERROR_BOUNDING_BOX_TOO_SMALL

Error message

The height and width of the bounding box is too small.

More information

The bounding box dimensions (height and width) have to be greater than 1 x 1 pixels.

During training, Amazon Rekognition Custom Labels resizes an image if any of its dimensions are greater than 1280 pixels (the source images aren't affected). The resulting bounding box heights and widths must be greater than 1 x 1 pixels. A bounding box location is stored in the annotations array of an object location JSON Line. For more information, see Object localization in manifest files 

	"bounding-box": {
		"image_size": [{
			"width": 640,
			"height": 480,
			"depth": 3
		}],
		"annotations": [{
			"class_id": 1,
			"top": 251,
			"left": 399,
			"width": 155,
			"height": 101
		}]
	},

The error information is added to the annotation object.

To fix ERROR_BOUNDING_BOX_TOO_SMALL

  • Choose one of the following options.

    • Increase the size of bounding boxes that are too small.

    • Remove bounding boxes that are too small. For information about removing a bounding box, see ERROR_TOO_MANY_BOUNDING_BOXES.

    • Remove the image (JSON Line) from the manifest.

ERROR_TOO_MANY_BOUNDING_BOXES

Error message

There are more bounding boxes than the allowed maximum.

More information

There are more bounding boxes than the allowed limit (50). You can remove excess bounding boxes in the Amazon Rekognition Custom Labels console, or you can remove them from the JSON Line.

To fix ERROR_TOO_MANY_BOUNDING_BOXES (Console).

  1. Decide which bounding boxes to remove.

  2. Open the Amazon Rekognition console at https://console.aws.amazon.com/rekognition/.

  3. Choose Use Custom Labels.

  4. Choose Get started.

  5. In the left navigation pane, choose the project that contains the dataset that you want to use.

  6. In the Datasets section, choose the dataset that you want to use.

  7. In the dataset gallery page, choose Start labeling to enter labeling mode.

  8. Choose the image that you want to remove bounding boxes from.

  9. Choose Draw bounding box.

  10. In the drawing tool, choose the bounding box that you want to delete.

  11. Press the delete key on your keyboard to delete the bounding box.

  12. Repeat the previous 2 steps until you have deleted enough bounding boxes.

  13. Choose Done

  14. Choose Save changes to save your changes.

  15. Choose Exit to exit labeling mode.

To fix ERROR_TOO_MANY_BOUNDING_BOXES (JSON Line).

  1. Open the manifest file and navigate to the JSON Line where the ERROR_TOO_MANY_BOUNDING_BOXES error occurs.

  2. Remove the following for each bounding box that you want to remove.

    • Remove the required annotation object from annotations array.

    • Remove the corresponding confidence object from the objects array in the label attribute metadata.

    • If no longer used by other bounding boxes, remove the label from the class-map.

    Use the following example to identify which items to remove.

    {
	"source-ref": "s3://custom-labels-bucket/images/IMG_1186.png",
	"bounding-box": {
		"image_size": [{
			"width": 640,
			"height": 480,
			"depth": 3
		}],
		"annotations": [
		   {              
			"class_id": 1,    #annotation object
			"top": 251,
			"left": 399,
			"width": 155,
			"height": 101
		}, {
			"class_id": 0,
			"top": 65,
			"left": 86,
			"width": 220,
			"height": 334
		}]
	},
	"bounding-box-metadata": {
		"objects": [
		>{                
			"confidence": 1          #confidence  object
		}, 
        {
			"confidence": 1
		}],
		"class-map": {  
			"0": "Echo",    #label 
			"1": "Echo Dot"
		},
		"type": "groundtruth/object-detection",
		"human-annotated": "yes",
		"creation-date": "2018-10-18T22:18:13.527256",
		"job-name": "my job"
	}
}

WARNING_UNANNOTATED_RECORD

Warning Message

Record is unannotated.

More information

An image added to a dataset by using the Amazon Rekognition Custom Labels console wasn't labeled. The JSON line for the image isn't used for training. 

{
    "source-ref": "s3://bucket/images/IMG_1186.png",
    "warnings": [
        {
            "code": "WARNING_UNANNOTATED_RECORD",
            "message": "Record is unannotated."
        } 
    ]
}
To fix WARNING_UNANNOTATED_RECORD

WARNING_NO_ANNOTATIONS

Warning Message

No annotations provided.

More information

A JSON Line in Object Localization format doesn't contain any bounding box information, despite being annotated by a human (human-annotated = yes). The JSON Line is valid, but isn't used for training. For more information, see Understanding training and testing validation result manifests. 

{
    "source-ref": "s3://bucket/images/IMG_1186.png",
    "bounding-box": {
        "image_size": [
            {
                "width": 640,
                "height": 480,
                "depth": 3
            }
        ],
        "annotations": [
           
        ],
        "warnings": [
            {
                "code": "WARNING_NO_ATTRIBUTE_ANNOTATIONS",
                "message": "No attribute annotations were found."
            }
        ]
    },
    "bounding-box-metadata": {
        "objects": [
           
        ],
        "class-map": {
           
        },
        "type": "groundtruth/object-detection",
        "human-annotated": "yes",
        "creation-date": "2013-11-18 02:53:27",
        "job-name": "my job"
    },
    "warnings": [
        {
            "code": "WARNING_NO_ANNOTATIONS",
            "message": "No annotations were found."
        } 
    ]
}
To fix WARNING_NO_ANNOTATIONS

  • Choose one of the following options.

WARNING_NO_ATTRIBUTE_ANNOTATIONS

Warning Message

No attribute annotations provided.

More information

A JSON Line in Object Localization format doesn't contain any bounding box annotation information, despite being annotated by a human (human-annotated = yes). The annotations array is not present or is not populuated. The JSON Line is valid, but isn't used for training. For more information, see Understanding training and testing validation result manifests. 

{
    "source-ref": "s3://bucket/images/IMG_1186.png",
    "bounding-box": {
        "image_size": [
            {
                "width": 640,
                "height": 480,
                "depth": 3
            }
        ],
        "annotations": [
           
        ],
        "warnings": [
            {
                "code": "WARNING_NO_ATTRIBUTE_ANNOTATIONS",
                "message": "No attribute annotations were found."
            }
        ]
    },
    "bounding-box-metadata": {
        "objects": [
           
        ],
        "class-map": {
           
        },
        "type": "groundtruth/object-detection",
        "human-annotated": "yes",
        "creation-date": "2013-11-18 02:53:27",
        "job-name": "my job"
    },
    "warnings": [
        {
            "code": "WARNING_NO_ANNOTATIONS",
            "message": "No annotations were found."
        }
    ]
}
To fix WARNING_NO_ATTRIBUTE_ANNOTATIONS

  • Choose one of the following options.

    • Add one or more bounding box annotation objects to the JSON Line. For more information, see Object localization in manifest files.

    • Remove the bounding box attribute.

    • Remove the image (JSON Line) from the manifest. If other valid bounding box attributes exist in the JSON Line, you can instead remove just the invalid bounding box attribute from the JSON Line.

ERROR_UNSUPPORTED_USE_CASE_TYPE

Warning Message

More information

The value of the type field isn't groundtruth/image-classification or groundtruth/object-detection. For more information, see Creating a manifest file. 

{
    "source-ref": "s3://bucket/test_normal_8.jpg",
    "BB": {
        "annotations": [
            {
                "left": 1768,
                "top": 1007,
                "width": 448,
                "height": 295,
                "class_id": 0
            },
            {
                "left": 1794,
                "top": 1306,
                "width": 432,
                "height": 411,
                "class_id": 1
            },
            {
                "left": 2568,
                "top": 1346,
                "width": 710,
                "height": 305,
                "class_id": 2
            },
            {
                "left": 2571,
                "top": 1020,
                "width": 644,
                "height": 312,
                "class_id": 3
            }
        ],
        "image_size": [
            {
                "width": 4000,
                "height": 2667,
                "depth": 3
            }
        ]
    },
    "BB-metadata": {
        "job-name": "labeling-job/BB",
        "class-map": {
            "0": "comparator",
            "1": "pot_resistor",
            "2": "ir_phototransistor",
            "3": "ir_led"
        },
        "human-annotated": "yes",
        "objects": [
            {
                "confidence": 1
            },
            {
                "confidence": 1
            },
            {
                "confidence": 1
            },
            {
                "confidence": 1
            }
        ],
        "creation-date": "2021-06-22T09:58:34.811Z",
        "type": "groundtruth/wrongtype",
        "cl-errors": [
            {
                "code": "ERROR_UNSUPPORTED_USE_CASE_TYPE",
                "message": "The use case type of the BB-metadata label attribute metadata is unsupported. Check the type field."
            }
        ]
    },
    "cl-metadata": {
        "is_labeled": true
    },
    "cl-errors": [
        {
            "code": "ERROR_NO_VALID_LABEL_ATTRIBUTES",
            "message": "No valid label attributes found."
        }
    ]
}
To fix ERROR_UNSUPPORTED_USE_CASE_TYPE

  • Choose one of the following options:

    • Change the value of the typefield to groundtruth/image-classification or groundtruth/object-detection, depending on the type of model that you want to create. For more information, see Creating a manifest file.

    • Remove the image (JSON Line) from the manifest.

ERROR_INVALID_LABEL_NAME_LENGTH

More information

The length of a label name is too long. The maximum length is 256 characters.

To fix ERROR_INVALID_LABEL_NAME_LENGTH

  • Choose one of the following options:

    • Reduce the length of the label name to 256 characters or less.

    • Remove the image (JSON Line) from the manifest.