📋 Add documentation for Anomaly, Classification, and Segmentation

docs/source/cpp/models/anomaly_model.md

@@ -1,5 +1,12 @@
 # Anomaly Model
 
+The `AnomalyModel` is a generic OpenVINO model that aims to provide a single interface for all the exported models based on [Anomalib](https://github.com/openvinotoolkit/anomalib).
+
+Currently, the `AnomalyModel` supports the following models:
+
+- Padim
+- STFPM
+
 ```{eval-rst}
 .. doxygenclass:: AnomalyModel
 

docs/source/python/models/anomaly.md

@@ -1,5 +1,12 @@
 # Anomaly
 
+The `AnomalyModel` is a generic OpenVINO model that aims to provide a single interface for all the exported models based on [Anomalib](https://github.com/openvinotoolkit/anomalib).
+
+Currently, the `AnomalyModel` supports the following models:
+
+- Padim
+- STFPM
+
 ```{eval-rst}
 .. automodule:: model_api.models.anomaly
    :members:

docs/source/python/models/classification.md

@@ -1,5 +1,22 @@
 # Classification
 
+## Description
+
+The `ClassificationModel` is the OpenVINO wrapper for models exported from [OpenVINO Training Extensions](https://github.com/openvinotoolkit/training_extensions). It supports multi-label classification as well as hierarchical classification.
+
+## Model Specifications
+
+## Inputs
+
+A single input image of shape (H, W, 3) where H and W are the height and width of the image, respectively.
+
+## Outputs
+
+- `top_labels`: List of tuples containing the top labels of the classification model.
+- `saliency_map`: Saliency map of the input image.
+- `feature_vector`: Feature vector of the input image. This is useful for Active Learning.
+- `raw_scores`: Raw scores of the classification model.
+
 ```{eval-rst}
 .. automodule:: model_api.models.classification
    :members:

docs/source/python/models/segmentation.md

@@ -1,5 +1,20 @@
 # Segmentation
 
+The `SegmentationModel` is the OpenVINO wrapper for models exported from [OpenVINO Training Extensions](https://github.com/openvinotoolkit/training_extensions). It produces a segmentation mask for the input image.
+
+## Model Specifications
+
+### Inputs
+
+A single input image of shape (H, W, 3) where H and W are the height and width of the image, respectively.
+
+### Outputs
+
+- `resultImage`: Image with the segmentation mask.
+- `soft_prediction`: Soft prediction of the segmentation model.
+- `saliency_map`: Saliency map of the input image.
+- `feature_vector`: Feature vector of the input image. This is useful for Active Learning.
+
 ```{eval-rst}
 .. automodule:: model_api.models.segmentation
    :members:

model_api/python/model_api/models/anomaly.py

@@ -22,6 +22,40 @@
 
 
 class AnomalyDetection(ImageModel):
+    """Anomaly Detection model.
+
+    Generic anomaly detection model that acts as an inference wrapper for all the exported models from
+    Anomalib.
+
+    Args:
+        inference_adapter (InferenceAdapter): Inference adapter
+        configuration (dict, optional): Configuration parameters. Defaults to {}.
+        preload (bool, optional): Whether to preload the model. Defaults to False.
+
+    Example:
+        >>> from model_api.models import AnomalyDetection
+        >>> import cv2
+        >>> model = AnomalyDetection.create_model("./path_to_model.xml")
+        >>> image = cv2.imread("path_to_image.jpg")
+        >>> result = model.predict(image)
+            AnomalyResult(anomaly_map=array([[150, 150, 150, ..., 138, 138, 138],
+                [150, 150, 150, ..., 138, 138, 138],
+                [150, 150, 150, ..., 138, 138, 138],
+                ...,
+                [134, 134, 134, ..., 138, 138, 138],
+                [134, 134, 134, ..., 138, 138, 138],
+                [134, 134, 134, ..., 138, 138, 138]], dtype=uint8),
+                pred_boxes=None, pred_label='Anomaly',
+                pred_mask=array([[1, 1, 1, ..., 1, 1, 1],
+                    [1, 1, 1, ..., 1, 1, 1],
+                    [1, 1, 1, ..., 1, 1, 1],
+                    ...,
+                    [1, 1, 1, ..., 1, 1, 1],
+                    [1, 1, 1, ..., 1, 1, 1],
+                    [1, 1, 1, ..., 1, 1, 1]], dtype=uint8),
+                    pred_score=0.8536462108391619)
+    """
+
     __model__ = "AnomalyDetection"
 
     def __init__(

model_api/python/model_api/models/classification.py

@@ -23,6 +23,27 @@
 
 
 class ClassificationModel(ImageModel):
+    """Classification Model.
+
+    Args:
+        inference_adapter (InferenceAdapter): Inference adapter
+        configuration (dict, optional): Configuration parameters. Defaults to {}.
+        preload (bool, optional): Whether to preload the model. Defaults to False.
+
+    Example:
+        >>> from model_api.models import ClassificationModel
+        >>> import cv2
+        >>> model = ClassificationModel.create_model("./path_to_model.xml")
+        >>> image = cv2.imread("path_to_image.jpg")
+        >>> result = model.predict(image)
+        ClassificationResult(
+            top_labels=[(1, 'bicycle', 0.90176445), (6, 'car', 0.85433626), (7, 'cat', 0.60699755)],
+            saliency_map=array([], dtype=float64),
+            feature_vector=array([], dtype=float64),
+            raw_scores=array([], dtype=float64)
+        )
+    """
+
     __model__ = "Classification"
 
     def __init__(self, inference_adapter: InferenceAdapter, configuration: dict = {}, preload: bool = False):

model_api/python/model_api/models/segmentation.py

@@ -49,6 +49,24 @@ def create_hard_prediction_from_soft_prediction(
 
 
 class SegmentationModel(ImageModel):
+    """Segmentation Model.
+
+    Args:
+        inference_adapter (InferenceAdapter): Inference adapter
+        configuration (dict, optional): Configuration parameters. Defaults to {}.
+        preload (bool, optional): Whether to preload the model. Defaults to False.
+
+    Example:
+        >>> from model_api.models import SegmentationModel
+        >>> import cv2
+        >>> model = SegmentationModel.create_model("./path_to_model.xml")
+        >>> image = cv2.imread("path_to_image.jpg")
+        >>> result = model.predict(image)
+        ImageResultWithSoftPrediction(
+            ...
+        )
+    """
+
     __model__ = "Segmentation"
 
     def __init__(self, inference_adapter, configuration: dict = {}, preload=False):