# Result Object Structure

The AIServerModel and CloudServerModel classes return a result object that contains the inference results from the `predict` and `predict_batch` functions.

Example:

{% code overflow="wrap" %}

```javascript
let someResult = await someModel.predict(image);
console.log(someResult);
```

{% endcode %}

For example, the result can be structured like this:

{% code overflow="wrap" %}

```json
{
    "result": [
        [
            { "category_id": 1, "label": "foo", "score": 0.2 },
            { "category_id": 0, "label": "bar", "score": 0.1 }
        ],
        "frame123"
    ],
    "imageFrame": imageBitmap
}
```

{% endcode %}

**Accessing the Result Data**

* **Inference Results**: Access the main results using `someResult.result[0]`.
* **Frame Info / Number**: Get the frame information or frame number using `someResult.result[1]`.
* **Original Input Image**: Access the original input image with `someResult.imageFrame`.

***

## Inference Result Types

The inference results can be one of the following types:

* **Detection**: Contains `bbox`, `category_id`, `label`, `score`, optional `landmarks` and `mask` for instance segmentation and pose estimation.
* **Classification**: Contains `label` and `score` for single-label classification.
* **Multi-Label Classification**: Contains `classifier` name and a `results` array with multiple `{ label, score }` entries.
* **Segmentation Mask**: Contains `shape` and `data`, where `data` is a flat array (or `Uint8Array`) representing class IDs per pixel.
* **Pose Detection**: Contains `landmarks` array with each landmark having `category_id`, `landmark` coordinates, optional `score`, `label`, and connectivity information.

***

## Example Results

### Detection Result

Detection results include bounding boxes (`bbox`) along with category IDs, labels, and confidence scores. Optionally, masks and landmarks may be included for segmentation and pose.

{% code overflow="wrap" %}

```json
[
  {
    "category_id": 2,
    "label": "person",
    "score": 0.98,
    "bbox": [0.1, 0.2, 0.4, 0.8]
  },
  {
    "category_id": 3,
    "label": "dog",
    "score": 0.87,
    "bbox": [0.5, 0.3, 0.9, 0.7],
    "mask": {
      "width": 128,
      "height": 128,
      "data": "<RLE string>"
    }
  }
]
```

{% endcode %}

### Classification Result

Single-label classification results contain a `label`, `score`, and `category_id`.

{% code overflow="wrap" %}

```json
[
  {
    "category_id": 0,
    "label": "dog",
    "score": 0.76
  }
]
```

{% endcode %}

### Multi-Label Classification Result

Multi-label classification results include a classifier name and an array of label-score pairs.

{% code overflow="wrap" %}

```json
[
  {
    "classifier": "scene_tags",
    "results": [
      { "label": "beach", "score": 0.85 },
      { "label": "sunset", "score": 0.65 }
    ]
  }
]
```

{% endcode %}

### Segmentation Mask Result

Semantic segmentation results provide a `shape` and a `data` array, which is typically returned as a `Uint8Array`.

{% code overflow="wrap" %}

```json
[
  {
    "shape": [1, 256, 256],
    "data": [0, 1, 1, 0, /* ... repeated for 256*256 entries ... */]
  }
]
```

{% endcode %}

### Pose Detection Result

Pose detection results include `landmarks` for each detected person, where each landmark has coordinates, optional score and label, and connectivity information.

{% code overflow="wrap" %}

```json
[
  {
    "category_id": 0,
    "label": "person",
    "score": 0.98,
    "bbox": [0.1, 0.2, 0.4, 0.8],
    "landmarks": [
      {
        "category_id": 0,
        "landmark": [0.15, 0.25],
        "connect": [1],
        "label": "nose",
        "score": 0.99
      },
      {
        "category_id": 1,
        "landmark": [0.15, 0.35],
        "connect": [0],
        "label": "left_eye",
        "score": 0.97
      }
    ]
  }
]
```

{% endcode %}

***