YOLOv8

Ultralytics YOLOv8, an acclaimed real-time object detection model.

Check out the original repo here.

Supported Models and Pricing

We support the following YOLOv8 models.

Model	Description	Price per minute of input video (@ 30 fps)	Price per image
yolov8l	YOLOv8-Large	$0.027	$0.0027
yolov8n	YOLOv8-Nano	$0.010	$0.001
yolov8l-face	YOLOv8-Large trained on the WIDER FACE dataset	$0.056	$0.0056
yolov8n-face	YOLOv8-Nano trained on the WIDER FACE dataset	$0.010	$0.001
yolov8s-world	YOLOv8-Small used for open-vocabulary object detection (specify any object you want to detect)	$0.016	$0.0016
yolov8l-world	YOLOv8-Large used for open-vocabulary object detection (specify any object you want to detect)	$0.034	$0.0034

We charge rates for each model per minute of video at 30fps. If multiple models are passed via the models parameter, the price will be the sum of the prices for each model. Videos with a different fps (native, or specified via the fps parameter) will be pro-rated.

Output Format

The model returns a dictionary with the following structure:

{
  "boxes": [
    {
      "x1": 281.36834716796875,
      "y1": 114.6531982421875,
      "x2": 937.3942260742188,
      "y2": 712.1339111328125,
      "width": 656.02587890625,
      "height": 597.480712890625,
      "confidence": 0.9540532827377319,
      "class_number": 0,
      "class_name": "person"
    },
    {
      "x1": 932.1299438476562,
      "y1": 610.4577026367188,
      "x2": 1279.69775390625,
      "y2": 711.7876586914062,
      "width": 347.56781005859375,
      "height": 101.3299560546875,
      "confidence": 0.6923111081123352,
      "class_number": 56,
      "class_name": "chair"
    }
  ],
  "frame_number": 0
}

Each box in the "boxes" array represents a detected object. The fields "x1", "y1", "x2", "y2" represent the coordinates of the top left and bottom right corners of the bounding box respectively. The "width" and "height" fields represent the width and height of the bounding box. The "confidence" field represents the confidence score of the detection. The "class_number" and "class_name" fields represent the class of the detected object.

Classes

If you are using the yolov8l-world or yolov8s-world models, you can specify any object you want to detect using the classes parameter.

Otherwise, there are 80 classes to choose from for detections. These are the possible detection classes:

"classes": {
  0: "person",
  1: "bicycle",
  2: "car",
  3: "motorcycle",
  4: "airplane",
  5: "bus",
  6: "train",
  7: "truck",
  8: "boat",
  9: "traffic light",
  10: "fire hydrant",
  11: "stop sign",
  12: "parking meter",
  13: "bench",
  14: "bird",
  15: "cat",
  16: "dog",
  17: "horse",
  18: "sheep",
  19: "cow",
  20: "elephant",
  21: "bear",
  22: "zebra",
  23: "giraffe",
  24: "backpack",
  25: "umbrella",
  26: "handbag",
  27: "tie",
  28: "suitcase",
  29: "frisbee",
  30: "skis",
  31: "snowboard",
  32: "sports ball",
  33: "kite",
  34: "baseball bat",
  35: "baseball glove",
  36: "skateboard",
  37: "surfboard",
  38: "tennis racket",
  39: "bottle",
  40: "wine glass",
  41: "cup",
  42: "fork",
  43: "knife",
  44: "spoon",
  45: "bowl",
  46: "banana",
  47: "apple",
  48: "sandwich",
  49: "orange",
  50: "broccoli",
  51: "carrot",
  52: "hot dog",
  53: "pizza",
  54: "donut",
  55: "cake",
  56: "chair",
  57: "couch",
  58: "potted plant",
  59: "bed",
  60: "dining table",
  61: "toilet",
  62: "tv",
  63: "laptop",
  64: "mouse",
  65: "remote",
  66: "keyboard",
  67: "cell phone",
  68: "microwave",
  69: "oven",
  70: "toaster",
  71: "sink",
  72: "refrigerator",
  73: "book",
  74: "clock",
  75: "vase",
  76: "scissors",
  77: "teddy bear",
  78: "hair drier",
  79: "toothbrush"
}