Scenes-with-text Detection (v4.1)
About this version
- Submitter: keighrim
- Submission Time: 2024-03-07T03:29:41+00:00
- Prebuilt Container Image: ghcr.io/clamsproject/app-swt-detection:v4.1
-
Release Notes
This version includes many bugfixes and clarification for the previous version.
- more informative, consistent, and flask-friendly debug-level logging for future development
- two additional pretrained models, including one based on convnext_tiny backbone for quicker annotation
TimePoint
annotations is re-worked- classification-related props in TP anns are now all based on the “RAW” labels from classifier
- all images classification results are now recorded as TP annotations regardless of TF annotations
- added two runtime parameter
useStitcher
- when"false"
, app will only generateTimePoint
annotations, not stitching them intoTimeFrame
annotationsmodelName
- to pick a model between pre-built classifiers (by default, app will use the best performing model from training experiments)
- updated to the latest
mmif-python
andclams-python
, and thus no longer generates MMIFs with a non-existing version
About this app (See raw metadata.json)
Detects scenes with text, like slates, chyrons and credits.
- App ID: http://apps.clams.ai/swt-detection/v4.1
- App License: Apache 2.0
- Source Repository: https://github.com/clamsproject/app-swt-detection (source tree of the submitted version)
Inputs
(Note: “*” as a property value means that the property is required but can be any value.)
- http://mmif.clams.ai/vocabulary/VideoDocument/v1 (required) (any properties)
Configurable Parameters
(Note: Multivalued means the parameter can have one or more values.)
Name | Description | Type | Multivalued | Default | Choices |
---|---|---|---|---|---|
startAt | Number of milliseconds into the video to start processing | integer | N | 0 | |
stopAt | Number of milliseconds into the video to stop processing | integer | N | 10000000 | |
sampleRate | Milliseconds between sampled frames | integer | N | 1000 | |
minFrameScore | Minimum score for a still frame to be included in a TimeFrame | number | N | 0.01 | |
minTimeframeScore | Minimum score for a TimeFrame | number | N | 0.5 | |
minFrameCount | Minimum number of sampled frames required for a TimeFrame | integer | N | 2 | |
modelName | model name to use for classification | string | N | 20240126-180026.convnext_lg.kfold_000 | 20240126-180026.convnext_lg.kfold_000 , 20240212-132306.convnext_lg.kfold_000 , 20240212-131937.convnext_tiny.kfold_000 |
useStitcher | Use the stitcher after classifying the TimePoints | boolean | N | true | false , true |
pretty | The JSON body of the HTTP response will be re-formatted with 2-space indentation | boolean | N | false | false , true |
Outputs
(Note: “*” as a property value means that the property is required but can be any value.)
(Note: Not all output annotations are always generated.)
- http://mmif.clams.ai/vocabulary/TimeFrame/v3
- timeUnit = “milliseconds”
- http://mmif.clams.ai/vocabulary/TimePoint/v2
- timeUnit = “milliseconds”
- labelset = a list of [“B”, “S”, “S:H”, “S:C”, “S:D”, “S:B”, “S:G”, “W”, “L”, “O”, “M”, “I”, “N”, “E”, “P”, “Y”, “K”, “G”, “T”, “F”, “C”, “R”]