docs: update classification, segmentation and style transfer

Author: JakubGonera

docs/docs/computer-vision/useObjectDetection.md

@@ -60,13 +60,13 @@ For more information on that topic, you can check out the [Loading models](https
 
 The hook returns an object with the following properties:
 
-| Field              | Type                                      | Description                                                                              |
-| ------------------ | ----------------------------------------- | ---------------------------------------------------------------------------------------- |
-| `forward`          | `(input: string) => Promise<Detection[]>` | A function that accepts an image (url, b64) and returns an array of `Detection` objects. |
-| `error`            | <code>string &#124; null</code>           | Contains the error message if the model loading failed.                                  |
-| `isGenerating`     | `boolean`                                 | Indicates whether the model is currently processing an inference.                        |
-| `isReady`          | `boolean`                                 | Indicates whether the model has successfully loaded and is ready for inference.          |
-| `downloadProgress` | `number`                                  | Represents the download progress as a value between 0 and 1.                             |
+| Field              | Type                                                                       | Description                                                                                                                                                              |
+| ------------------ | -------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `forward`          | `(input: string, detectionThreshold: float = 0.7) => Promise<Detection[]>` | A function that accepts an image (url, b64) and returns an array of `Detection` objects. `detectionThreshold` can be supplied to alter the sensitivity of the detection. |
+| `error`            | <code>string &#124; null</code>                                            | Contains the error message if the model loading failed.                                                                                                                  |
+| `isGenerating`     | `boolean`                                                                  | Indicates whether the model is currently processing an inference.                                                                                                        |
+| `isReady`          | `boolean`                                                                  | Indicates whether the model has successfully loaded and is ready for inference.                                                                                          |
+| `downloadProgress` | `number`                                                                   | Represents the download progress as a value between 0 and 1.                                                                                                             |
 
 ## Running the model
 

docs/docs/typescript-api/ClassificationModule.md

@@ -14,11 +14,13 @@ import {
 
 const imageUri = 'path/to/image.png';
 
+const module = new ClassificationModule();
+
 // Loading the model
-await ClassificationModule.load(EFFICIENTNET_V2_S);
+await module.load(EFFICIENTNET_V2_S);
 
 // Running the model
-const classesWithProbabilities = await ClassificationModule.forward(imageUri);
+const classesWithProbabilities = await module.forward(imageUri);
 ```
 
 ### Methods
@@ -40,8 +42,8 @@ type ResourceSource = string | number | object;
 
 ## Loading the model
 
-To load the model, use the `load` method. It accepts the `modelSource` which is a string that specifies the location of the model binary. For more information, take a look at [loading models](../fundamentals/loading-models.md) page. This method returns a promise, which can resolve to an error or void.
+To load the model, create a new instance of the module and use the `load` method on it. It accepts the `modelSource` which is a string that specifies the location of the model binary. For more information, take a look at [loading models](../fundamentals/loading-models.md) page. This method returns a promise, which can resolve to an error or void.
 
 ## Running the model
 
-To run the model, you can use the `forward` method. It accepts one argument, which is the image. The image can be a remote URL, a local file URI, or a base64-encoded image. The method returns a promise, which can resolve either to an error or an object containing categories with their probabilities.
+To run the model, you can use the `forward` method on the module object. It accepts one argument, which is the image. The image can be a remote URL, a local file URI, or a base64-encoded image. The method returns a promise, which can resolve either to an error or an object containing categories with their probabilities.

docs/docs/typescript-api/ImageSegmentationModule.md

@@ -14,11 +14,13 @@ import {
 
 const imageUri = 'path/to/image.png';
 
+const module = new ImageSegmentationModule();
+
 // Loading the model
-await ImageSegmentationModule.load(DEEPLAB_V3_RESNET50);
+await module.load(DEEPLAB_V3_RESNET50);
 
 // Running the model
-const outputDict = await ImageSegmentationModule.forward(imageUri);
+const outputDict = await module.forward(imageUri);
 ```
 
 ### Methods
@@ -40,11 +42,11 @@ type ResourceSource = string | number | object;
 
 ## Loading the model
 
-To load the model, use the `load` method. It accepts the `modelSource` which is a string that specifies the location of the model binary. For more information, take a look at [loading models](../fundamentals/loading-models.md) page. This method returns a promise, which can resolve to an error or void.
+To load the model, create a new instance of the module and use the `load` method on it. It accepts the `modelSource` which is a string that specifies the location of the model binary. For more information, take a look at [loading models](../fundamentals/loading-models.md) page. This method returns a promise, which can resolve to an error or void.
 
 ## Running the model
 
-To run the model, you can use the `forward` method. It accepts three arguments: a required image, an optional list of classes, and an optional flag whether to resize the output to the original dimensions.
+To run the model, you can use the `forward` method on the module object. It accepts three arguments: a required image, an optional list of classes, and an optional flag whether to resize the output to the original dimensions.
 
 - The image can be a remote URL, a local file URI, or a base64-encoded image.
 - The `classesOfInterest` list contains classes for which to output the full results. By default the list is empty, and only the most probable classes are returned (essentially an arg max for each pixel). Look at `DeeplabLabel` enum for possible classes.

docs/docs/typescript-api/StyleTransferModule.md

@@ -14,11 +14,13 @@ import {
 
 const imageUri = 'path/to/image.png';
 
+const module = new StyleTransferModule();
+
 // Loading the model
-await StyleTransferModule.load(STYLE_TRANSFER_CANDY);
+await module.load(STYLE_TRANSFER_CANDY);
 
 // Running the model
-const generatedImageUrl = await StyleTransferModule.forward(imageUri);
+const generatedImageUrl = await module.forward(imageUri);
 ```
 
 ### Methods
@@ -40,8 +42,8 @@ type ResourceSource = string | number | object;
 
 ## Loading the model
 
-To load the model, use the `load` method. It accepts the `modelSource` which is a string that specifies the location of the model binary. For more information, take a look at [loading models](../fundamentals/loading-models.md) page. This method returns a promise, which can resolve to an error or void.
+To load the model, create a new instance of the module and use the `load` method on it. It accepts the `modelSource` which is a string that specifies the location of the model binary. For more information, take a look at [loading models](../fundamentals/loading-models.md) page. This method returns a promise, which can resolve to an error or void.
 
 ## Running the model
 
-To run the model, you can use the `forward` method. It accepts one argument, which is the image. The image can be a remote URL, a local file URI, or a base64-encoded image. The method returns a promise, which can resolve either to an error or a URL to generated image.
+To run the model, you can use the `forward` method on the module object. It accepts one argument, which is the image. The image can be a remote URL, a local file URI, or a base64-encoded image. The method returns a promise, which can resolve either to an error or a URL to generated image.