import { pipeline } from'@xenova/transformers';
const detector = awaitpipeline('zero-shot-object-detection', 'Xenova/owlv2-base-patch16-ensemble');
const url = 'http://images.cocodataset.org/val2017/000000039769.jpg';
const candidate_labels = ['a photo of a cat', 'a photo of a dog'];
const output = awaitdetector(url, candidate_labels);
console.log(output);
// [// { score: 0.7400985360145569, label: 'a photo of a cat', box: { xmin: 0, ymin: 50, xmax: 323, ymax: 485 } },// { score: 0.6315087080001831, label: 'a photo of a cat', box: { xmin: 333, ymin: 23, xmax: 658, ymax: 378 } }// ]
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using
🤗 Optimum
and structuring your repo like this one (with ONNX weights located in a subfolder named
onnx
).
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