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@huggingface/tasks

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import type { TaskDataCustom } from "../index.js"; const taskData: TaskDataCustom = { datasets: [ { // TODO write proper description description: "", id: "", }, ], demo: { inputs: [ { filename: "image-classification-input.jpeg", type: "img", }, { label: "Classes", content: "cat, dog, bird", type: "text", }, ], outputs: [ { type: "chart", data: [ { label: "Cat", score: 0.664, }, { label: "Dog", score: 0.329, }, { label: "Bird", score: 0.008, }, ], }, ], }, metrics: [ { description: "Computes the number of times the correct label appears in top K labels predicted", id: "top-K accuracy", }, ], models: [ { description: "Multilingual image classification model for 80 languages.", id: "visheratin/mexma-siglip", }, { description: "Strong zero-shot image classification model.", id: "google/siglip2-base-patch16-224", }, { description: "Robust zero-shot image classification model.", id: "intfloat/mmE5-mllama-11b-instruct", }, { description: "Powerful zero-shot image classification model supporting 94 languages.", id: "jinaai/jina-clip-v2", }, { description: "Strong image classification model for biomedical domain.", id: "microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224", }, ], spaces: [ { description: "An application that leverages zero-shot image classification to find best captions to generate an image. ", id: "pharma/CLIP-Interrogator", }, { description: "An application to compare different zero-shot image classification models. ", id: "merve/compare_clip_siglip", }, ], summary: "Zero-shot image classification is the task of classifying previously unseen classes during training of a model.", widgetModels: ["google/siglip-so400m-patch14-224"], youtubeId: "", }; export default taskData;