Datasets:

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ftopal

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huggingface-models-embeddings

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ERROR: type should be string, got "\nhttps://huggingface.co/google/owlv2-base-patch16-ensemble with ONNX weights to be compatible with Transformers.js.\n\n\n## Usage (Transformers.js)\n\nIf you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:\n```bash\nnpm i @xenova/transformers\n```\n\n**Example:** Zero-shot object detection w/ `Xenova/owlv2-base-patch16-ensemble`.\n```js\nimport { pipeline } from '@xenova/transformers';\n\nconst detector = await pipeline('zero-shot-object-detection', 'Xenova/owlv2-base-patch16-ensemble');\n\nconst url = 'http://images.cocodataset.org/val2017/000000039769.jpg';\nconst candidate_labels = ['a photo of a cat', 'a photo of a dog'];\nconst output = await detector(url, candidate_labels);\nconsole.log(output);\n// [\n// { score: 0.7400985360145569, label: 'a photo of a cat', box: { xmin: 0, ymin: 50, xmax: 323, ymax: 485 } },\n// { score: 0.6315087080001831, label: 'a photo of a cat', box: { xmin: 333, ymin: 23, xmax: 658, ymax: 378 } }\n// ]\n```\n\n\n\n---\n\nNote: 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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`)."

ERROR: type should be string, got "\nhttps://huggingface.co/google/owlv2-base-patch16 with ONNX weights to be compatible with Transformers.js.\n\nNote: 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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`)."

ERROR: type should be string, got "\nhttps://huggingface.co/google/owlv2-base-patch16-finetuned with ONNX weights to be compatible with Transformers.js.\n\n\n## Usage (Transformers.js)\n\nIf you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:\n```bash\nnpm i @xenova/transformers\n```\n\n**Example:** Zero-shot object detection w/ `Xenova/owlv2-base-patch16-finetuned`.\n```js\nimport { pipeline } from '@xenova/transformers';\n\nconst detector = await pipeline('zero-shot-object-detection', 'Xenova/owlv2-base-patch16-finetuned');\n\nconst url = 'http://images.cocodataset.org/val2017/000000039769.jpg';\nconst candidate_labels = ['a photo of a cat', 'a photo of a dog'];\nconst output = await detector(url, candidate_labels);\nconsole.log(output);\n// [\n// { score: 0.6951543688774109, label: 'a photo of a cat', box: { xmin: 326, ymin: 23, xmax: 650, ymax: 376 } },\n// { score: 0.5766839385032654, label: 'a photo of a cat', box: { xmin: 6, ymin: 63, xmax: 315, ymax: 487 } }\n// ]\n```\n\n\n\n---\n\nNote: 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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`)."