docs: Add object detection docs

docs/docs/computer_vision/_category_.json

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+{
+    "label": "Computer Vision",
+    "position": 3,
+    "link": {
+      "type": "generated-index"
+    }
+}
+

docs/docs/computer_vision/useObjectDetection.mdx

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+---
+title: useObjectDetection
+sidebar_position: 1
+---
+
+`useObjectDetection` is a hook that allows you to seamlessly integrate object detection into your React Native application. Currently, the SSDLite320Large model with a MobileNetv3 backbone is supported.
+
+## Reference
+```jsx
+import { useObjectDetection, SSDLITE_320_MOBILENET_V3_LARGE_URL } from 'react-native-executorch';
+
+function App() {
+  const ssdlite = useObjectDetection({
+    modelSource: SSDLITE_320_MOBILENET_V3_LARGE_URL, // alternatively, you can use require(...)
+  });
+
+  ...
+  for (const detection of await ssdlite.forward("https://url-to-image.jpg")) {
+    console.log("Bounding box: ", detection.bbox);
+    console.log("Bounding label: ", detection.label);
+    console.log("Bounding score: ", detection.score);
+  }
+  ...
+}
+```
+
+<details>
+<summary>Type definitions</summary>
+
+```typescript
+interface Bbox {
+  x1: number;
+  x2: number;
+  y1: number;
+  y2: number;
+}
+
+interface Detection {
+  bbox: Bbox;
+  label: keyof typeof CocoLabel;
+  score: number;
+}
+
+interface ObjectDetectionModule {
+  error: string | null;
+  isModelReady: boolean;
+  isModelGenerating: boolean;
+  forward: (input: string) => Promise<Detection[]>;
+}
+```
+</details>
+
+### 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 function returns an array of `Detection` objects. Each object contains coordinates of the bounding box, the label of the detected object, and the confidence score. For more information, please refer to the reference or type definitions.
+
+### Arguments
+
+`modelSource`
+
+A string that specifies the path to the model file. You can download the model from our [HuggingFace repository](https://huggingface.co/software-mansion/react-native-executorch-ssdlite320-mobilenet-v3-large/tree/main). 
+
+Since the `.pte` file for this model is less than 512MB, you can add it to your assets directory and use `require()`. Alternatively, if you prefer to download the model at runtime instead of bundling it, you can use the constants provided with the library.
+
+
+### Returns
+
+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 failed to load or encountered an error during the generation process.    |
+| `isModelGenerating`  | `boolean`                            | Indicates whether the model is currently processing an inference.                                               |
+| `isModelReady`       | `boolean`                            | Indicates whether the model has successfully loaded and is ready for inference.                                 |
+
+
+
+### Detection object
+The detection object is specified as follows:
+```typescript
+interface Bbox {
+  x1: number;
+  y1: number;
+  x2: number;
+  y2: number;
+}
+
+interface Detection {
+  bbox: Bbox;
+  label: keyof typeof CocoLabels;
+  score: number;
+}
+```
+The `bbox` property contains information about the bounding box of detected objects. It is represented as two points: one at the bottom-left corner of the bounding box (`x1`, `y1`) and the other at the top-right corner (`x2`, `y2`).
+The `label` property contains the name of the detected object, which corresponds to one of the `CocoLabels`. The `score` represents the confidence score of the detected object.
+
+
+### Example
+```tsx
+import { useObjectDetection, SSDLITE_320_MOBILENET_V3_LARGE_URL } from 'react-native-executorch';
+
+function App() {
+  const ssdlite = useObjectDetection({
+    modelSource: SSDLITE_320_MOBILENET_V3_LARGE_URL,
+  });
+
+  const runModel = async () => {
+    const detections = await ssdlite.forward("https://url-to-image.jpg");
+    for (const detection of detections) {
+      console.log("Bounding box: ", detection.bbox);
+      console.log("Bounding label: ", detection.label);
+      console.log("Bounding score: ", detection.score);
+    }
+  }
+}
+```