Input Data Quotas
Input datasets used in semantic segmentation labeling jobs have a quota of 20,000 items. For all other labeling job types, the dataset size quota is 100,000 items. To request an increase to the quota for labeling jobs other than semantic segmentation jobs, review the procedures in AWS Service Quotas to request a quota increase.
Input image data for active and non-active learning labeling jobs must not exceed size and resolution quotas. Active learning refers to labeling job that use automated data labeling. Non-active learning refers to labeling jobs that don't use automated data labeling.
Additional quotas apply for label categories for all task types, and for input data and labeling category attributes for 3D point cloud and video frame task types.
Input File Size Quota
Input files can't exceed the following size- quotas for both active and non-active learning labeling jobs. There is no input file size quota for videos used in video classification labeling jobs.
| Labeling Job Task Type | Input File Size Quota |
|---|---|
| Image classification | 40 MB |
| Bounding box (Object detection) | 40 MB |
| Semantic segmentation | 40 MB |
| Bounding box (Object detection) label adjustment | 40 MB |
| Semantic segmentation label adjustment | 40 MB |
| Bounding box (Object detection) label verification | 40 MB |
| Semantic segmentation label verification | 40 MB |
Input Image Resolution Quotas
Image file resolution refers to the number of pixels in an image, and determines the amount of detail an image holds. Image resolution quotas differ depending on the labeling job type and the SageMaker built-in algorithm used. The following table lists the resolution quotas for images used in active and non-active learning labeling jobs.
| Labeling Job Task Type | Resolution Quota - Non Active Learning | Resolution Quota - Active Learning |
|---|---|---|
| Image classification | 100 million pixels | 3840 x 2160 pixels (4 K) |
| Bounding box (Object detection) | 100 million pixels | 3840 x 2160 pixels (4 K) |
| Semantic segmentation | 100 million pixels | 1920 x 1080 pixels (1080 p) |
| Object detection label adjustment | 100 million pixels | 3840 x 2160 pixels (4 K) |
| Semantic segmentation label adjustment | 100 million pixels | 1920 x 1080 pixels (1080 p) |
| Object detection label verification | 100 million pixels | Not available |
| Semantic segmentation label verification | 100 million pixels | Not available |
Label Category Quotas
Each labeling job task type has a quota for the number of label categories you can specify. Workers select label categories to create annotations. For example, you may specify label categories car, pedestrian, and biker when creating a bounding box labeling job and workers will select the car category before drawing bounding boxes around cars.
Important
Label category names cannot exceed 256 characters.
All label categories must be unique. You cannot specify duplicate label categories.
The following label category limits apply to labeling jobs. Quotas for label
categories depend on whether you use the SageMaker API operation
CreateLabelingJob or the console to create a labeling
job.
| Labeling Job Task Type | Label Category Quota - API | Label Category Quota - Console |
|---|---|---|
| Image classification (Multi-label) | 50 | 50 |
| Image classification (Single label) | Unlimited | 30 |
| Bounding box (Object detection) | 50 | 50 |
| Label verification | Unlimited | 30 |
| Semantic segmentation (with active learning) | 20 | 10 |
| Semantic segmentation (without active learning) | Unlimited | 10 |
| Named entity recognition | Unlimited | 30 |
| Text classification (Multi-label) | 50 | 50 |
| Text classification (Single label) | Unlimited | 30 |
| Video classification | 30 | 30 |
| Video frame object detection | 30 | 30 |
| Video frame object tracking | 30 | 30 |
| 3D point cloud object detection | 30 | 30 |
| 3D point cloud object tracking | 30 | 30 |
| 3D point cloud semantic segmentation | 30 | 30 |
3D Point Cloud and Video Frame Labeling Job Quotas
The following quotas apply for 3D point cloud and video frame labeling job input data.
| Labeling Job Task Type | Input Data Quota |
|---|---|
| Video frame object detection | 2,000 video frames (images) per sequence |
| Video frame object detection | 10 video frame sequences per manifest file |
| Video frame object tracking | 2,000 video frames (images) per sequence |
| Video frame object tracking | 10 video frame sequences per manifest file |
| 3D point cloud object detection | 100,000 point cloud frames per labeling job |
| 3D point cloud object tracking | 100,000 point cloud frame sequences per labeling job |
| 3D point cloud object tracking | 500 point cloud frames in each sequence file |
When you create a video frame or 3D point cloud labeling job, you can add one or more label category attributes to each label category that you specify to have workers provide more information about an annotation.
Each label category attribute has a single label category attribute
name, and a list of one or more options (values) to choose
from. To learn more, see Worker user interface (UI) for 3D point cloud labeling
jobs and Worker user interface (UI) for video frame labeling jobs.
The following quotas apply to the number of label category attributes names and values you can specify for labeling jobs.
| Labeling Job Task Type | Label Category Attribute (name) Quota | Label Category Attribute Values Quota |
|---|---|---|
| Video frame object detection | 10 | 10 |
| Video frame object tracking | 10 | 10 |
| 3D point cloud object detection | 10 | 10 |
| 3D point cloud object tracking | 10 | 10 |
| 3D point cloud semantic segmentation | 10 | 10 |
