# Validation rules for manifest files
<a name="md-create-manifest-file-validation-rules"></a>

 When you import a manifest file, Amazon Rekognition Custom Labels applies validation rules for limits, syntax, and semantics. The SageMaker AI Ground Truth schema enforces syntax validation. For more information, see [Output](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-data-output.html). The following are the validation rules for limits and semantics.

**Note**  
The 20% invalidity rules apply cumulatively across all validation rules. If the import exceeds the 20% limit due to any combination, such as 15% invalid JSON and 15% invalid images, the import fails. 
Each dataset object is a line in the manifest. Blank/invalid lines are also counted as dataset objects.
Overlaps are (common labels between test and train)/(train labels).

**Topics**
+ [Limits](#md-validation-rules-limits)
+ [Semantics](#md-validation-rules-semantics)

## Limits
<a name="md-validation-rules-limits"></a>


| Validation | Limit | Error raised | 
| --- | --- | --- | 
| Manifest file size | Maximum 1 GB | Error | 
| Maximum line count for a manifest file | Maximum of 250,000 dataset objects as lines in a manifest.  | Error | 
| Lower boundary on total number of valid dataset objects per label  | >= 1 | Error | 
| Lower boundary on labels | >=2 | Error | 
| Upper bound on labels | <= 250 | Error | 
| Minimum bounding boxes per image | 0 | None | 
| Maximum bounding boxes per image | 50 | None | 

## Semantics
<a name="md-validation-rules-semantics"></a>




| Validation | Limit | Error raised | 
| --- | --- | --- | 
| Empty manifest |  | Error | 
| Missing/in-accessible source-ref object | Number of objects less than 20% | Warning | 
| Missing/in-accessible source-ref object | Number of objects > 20% | Error | 
| Test labels not present in training dataset  | At least 50% overlap in the labels | Error | 
| Mix of label vs. object examples for same label in a dataset. Classification and detection for the same class in a dataset object.  |  | No error or warning | 
| Overlapping assets between test and train  | There should not be an overlap between test and training datasets.  |  | 
| Images in a dataset must be from same bucket  | Error if the objects are in a different bucket | Error |