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import type { TaskDataCustom } from "../Types";
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: "Robust image classification model trained on publicly available image-caption data.",
id: "openai/clip-vit-base-patch16",
},
{
description:
"Robust image classification model trained on publicly available image-caption data trained on additional high pixel data for better performance.",
id: "openai/clip-vit-large-patch14-336",
},
{
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",
},
],
summary:
"Zero shot image classification is the task of classifying previously unseen classes during training of a model.",
widgetModels: ["openai/clip-vit-large-patch14-336"],
youtubeId: "",
};
export default taskData;
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