Results

Here's an example of the result of an analysis performed by Foyer Insight:

{
  "classifier": [
    {
      "confidence": 0.9996496438980103,
      "name": "Kitchen",
      "rank": 1
    },
    {
      "confidence": 0.9999802112579346,
      "name": "Indoor",
      "rank": 2
    }
  ],
  "detections": [
    {
      "area": 55518,
      "attributes": {
        "is-stainless": false
      },
      "bbox": [
        3,
        156,
        564,
        356
      ],
      "class": 164,
      "classId": 164,
      "score": 0.8126423358917236
    },
    {
      "area": 66851,
      "attributes": {
        "is-stainless": false
      },
      "bbox": [
        0,
        3,
        540,
        271
      ],
      "class": 86,
      "classId": 86,
      "score": 0.7137435674667358
    }
    ...
  ]
}

Let's break down the sections we're seeing.

Classifier

Each classifier has three fields: confidence, name, and rank.

• Confidence is how certain Foyer Insight is of the classification. This is a number between 0 and 1, which alternatively can be thought of as a percentage. In this case, Insight is almost certain we've provided an image of an indoor kitchen.

• Name is the full name of the image's classification.

• Rank is a number greater than zero. The classification with rank 1 is always the one with the highest confidence.

Detections

Each detection has six fields: area, attributes, bbox, class, classId, and score. A detection is an individual object or group of related objects that Insight found in the image.

• Area is the total number of pixels a detection takes up in the entire image. For example, given the above result, 55518 pixels in the image are detected as being part of a table.

• Attributes are extra details about the detection. In our result, no objects were detected as being made of stainless steel.

• BBox is the bounding box of the detection. It is made up of 4 numbers, which are really 2 (x, y) coordinate pairs representing the upper left and bottom right corners respectively. For example, the values [3, 156, 564, 356] represent a point at (3, 156) and (564, 356).

• Class is the full name of the detection's class.

• ClassID is the integer ID that corresponds to a particular class name.

• Score is how certain Insight is of the class for a detection. As with a classifier's score, it is a number between 0 and 1. In our example, Insight is about 81% certain that its detection of a table is correct.

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