@huggingface/tasks/dist/esm/tasks/zero-shot-object-detection/data.js

List of ML tasks for huggingface.co/tasks

const taskData = {
    datasets: [],
    demo: {
        inputs: [
            {
                filename: "zero-shot-object-detection-input.jpg",
                type: "img",
            },
            {
                label: "Classes",
                content: "cat, dog, bird",
                type: "text",
            },
        ],
        outputs: [
            {
                filename: "zero-shot-object-detection-output.jpg",
                type: "img",
            },
        ],
    },
    metrics: [
        {
            description: "The Average Precision (AP) metric is the Area Under the PR Curve (AUC-PR). It is calculated for each class separately",
            id: "Average Precision",
        },
        {
            description: "The Mean Average Precision (mAP) metric is the overall average of the AP values",
            id: "Mean Average Precision",
        },
        {
            description: "The APα metric is the Average Precision at the IoU threshold of a α value, for example, AP50 and AP75",
            id: "APα",
        },
    ],
    models: [
        {
            description: "Solid zero-shot object detection model.",
            id: "IDEA-Research/grounding-dino-base",
        },
        {
            description: "Cutting-edge zero-shot object detection model.",
            id: "google/owlv2-base-patch16-ensemble",
        },
    ],
    spaces: [
        {
            description: "A demo to try the state-of-the-art zero-shot object detection model, OWLv2.",
            id: "merve/owlv2",
        },
        {
            description: "A demo that combines a zero-shot object detection and mask generation model for zero-shot segmentation.",
            id: "merve/OWLSAM",
        },
    ],
    summary: "Zero-shot object detection is a computer vision task to detect objects and their classes in images, without any prior training or knowledge of the classes. Zero-shot object detection models receive an image as input, as well as a list of candidate classes, and output the bounding boxes and labels where the objects have been detected.",
    widgetModels: [],
    youtubeId: "",
};
export default taskData;