update md

docs/model-configuration.md

@@ -78,6 +78,17 @@ The list features only model wrappers which intoduce new configuration values in
 1. `blur_strength`: int - blurring kernel size. -1 value means no blurring and no soft_threshold
 1. `soft_threshold`: float - probability threshold value for bounding box filtering. inf value means no blurring and no soft_threshold
 1. `return_soft_prediction`: bool - return raw resized model prediction in addition to processed one
+### `ActionClassificationModel`
+1. `labels`: List - list of class labels
+1. `path_to_labels`: str - path to file with labels. Overrides the labels, if they sets via 'labels' parameter
+1. `mean_values`: List - normalization values, which will be subtracted from image channels for image-input layer during preprocessing
+1. `pad_value`: int - pad value for resize_image_letterbox embedded into a model
+1. `resize_type`: str - crop, standard, fit_to_window or fit_to_window_letterbox
+1. `reverse_input_channels`: bool - reverse the input channel order
+1. `scale_values`: List - normalization values, which will divide the image channels for image-input layer
+
+> **NOTE** `ActionClassificationModel` isn't subclass of ImageModel.
+
 ### `Bert` and its subclasses
 1. `vocab`: Dict - mapping from string token to int
 1. `input_names`: str - comma-separated names of input layers

model_api/python/README.md

@@ -70,6 +70,7 @@ The following tasks can be solved with wrappers usage:
 | Question Answering         |  <ul><li>`BertQuestionAnswering`</li></ul> |
 | Salient Object Detection   |  <ul><li>`SalientObjectDetectionModel`</li></ul> |
 | Semantic Segmentation      |  <ul><li>`SegmentationModel`</li></ul> |
+| Action Classification      |  <ul><li>`ActionClassificationModel`</li></ul> |
 
 ## Model API Adapters
 

model_api/python/docs/ActionClassification.md

@@ -0,0 +1,79 @@
+# ActionClassification Wrapper
+
+## Use Case and High-Level Description
+
+The `ActionClassificationModel` is a wrapper class designed for action classification models.
+This class provides support for data preprocessing and postprocessing like other model wrapper classes.
+Note that it isn't a subclass of `ImageModel`. It gets video as input so it is different than ImageModel.
+
+## How to use
+
+Utilizing the ActionClassificationModel is similar to other model wrappers, with the primary difference being the preparation of video clip inputs instead of single images.
+
+Below is an example demonstrating how to initialize the model with OpenVINO™ IR files and classify actions in a video clip.
+
+
+```python
+import cv2
+import numpy as np
+# import model wrapper class
+from model_api.models import ActionClassificationModel
+# import inference adapter and helper for runtime setup
+from model_api.adapters import OpenvinoAdapter, create_core
+
+
+# load video and make a clip as input
+cap = cv2.VideoCapture("sample.mp4")
+input_data = np.stack([cap.read()[1] for _ in range(8)])
+
+
+# define the path to action classification model in IR format
+model_path = "action_classification.xml"
+
+# create adapter for OpenVINO™ runtime, pass the model path
+inference_adapter = OpenvinoAdapter(create_core(), model_path, device="CPU")
+
+# instantiate the ActionClassificationModel wrapper
+# setting preload=True loads the model onto the CPU within the adapter0
+action_cls_model = ActionClassificationModel(inference_adapter, preload=True)
+
+# perform preprocessing, inference, and postprocessing
+results = action_cls_model(input_data)
+```
+
+As illustrated, initializing the model and performing inference can be achieved with minimal code.
+The wrapper class takes care of input processing, layout adjustments, and output processing automatically.
+
+
+## Arguments
+
+- `labels`(`list[str]`) : List of class labels
+- `path_to_labels` (`str`) : Path to file with labels. Labels are overrided if it's set.
+- `mean_values` (`list[int | float]`) Normalization values to be subtracted from the image channels during preprocessing.
+- `pad_value` (`int`) Pad value used during the resize resize_image_letterbox operation embedded within the model.
+- `resize_type` (`str`) : The method of resizing the input image. Valid options include `crop`, `standard`, `fit_to_window`, and `fit_to_window_letterbox`.
+- `reverse_input_channels` (`bool`) : Whether to reverse the order of input channels.
+- `scale_values` (`list[int | float]`): Normalization values used to divide the image channels during preprocessing.
+
+## Input format
+
+The input format for action classification tasks differs from other vision tasks due to the nature of video data.
+The input tensor includes additional dimensions to accommodate the video format.
+It's often refered as single alphabet, and each alphabet means as below.
+
+- N : Batch size
+- S : Numer of clips x Number of crops
+- C : Number of channels
+- T : Time
+- H : Height
+- W : Width
+
+The input should be provided as a single clip in THWC format.
+Depending on the specified layout, the input will be transformed into either NSTHWC or NSCTHW format.
+Unlike other vision model wrappers that utilize OpenVINO™'s PrePostProcessors (PPP) for preprocessing,
+the ActionClassificationModel performs its preprocessing due to the current lack of video format support in OpenVINO™ PPP.
+
+## output format
+
+The output is encapsulated in a ClassificationResult object, which includes the indices, labels, and logits of the top predictions.
+At present, saliency maps, feature vectors, and raw scores are not provided.