Owlv2 Base Patch16 Finetuned
with ONNX weights to be compatible with Transformers.js.
Source du modèle
- Type
- Other
- Taille du fichier
- 122–586 MB
- Modèle de base
- google/owlv2-base-patch16-finetuned
- Version
- vRevision 5d38cb5
- Mis à jour
- 10 oct. 2026
- Téléchargements
- 33
- Source
- Hugging Face
Description de la source
https://huggingface.co/google/owlv2-base-patch16-finetuned with ONNX weights to be compatible with Transformers.js.
Usage (Transformers.js)
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @huggingface/transformers
Example: Zero-shot object detection w/ Xenova/owlv2-base-patch16-finetuned.
import { pipeline } from '@huggingface/transformers';
const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlv2-base-patch16-finetuned');
const url = 'http://images.cocodataset.org/val2017/000000039769.jpg';
const candidate_labels = ['a photo of a cat', 'a photo of a dog'];
const output = await detector(url, candidate_labels);
console.log(output);
// [
// { score: 0.6951543688774109, label: 'a photo of a cat', box: { xmin: 326, ymin: 23, xmax: 650, ymax: 376 } },
// { score: 0.5766839385032654, label: 'a photo of a cat', box: { xmin: 6, ymin: 63, xmax: 315, ymax: 487 } }
// ]
image/png
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).
Sources
1 source
Sources
1 sourceVérifié 10 oct.
Download files
Artefacts du modèle
7 artefacts
Artefacts du modèle
7 artefactsExtraits de sources
3 extraits
Extraits de sources
3 extraits--- base_model: google/owlv2-base-patch16-finetuned library_name: transformers.js --- https://huggingface.co/google/owlv2-base-patch16-finetuned with ONNX weights to be compatible with Transformers.js. ## Usage (Transformers.js) If 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/@huggingface/transformers) using: ```bash npm i @huggingface/transformers ``` **Example:** Zero-shot object detection w/ `Xenova/owlv2-base-patch16-finetuned`. ```js import { pipeline } from '@huggingface/transformers'; const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlv2-base-patch16-finetuned'); const url = 'http://images.cocodataset.org/val2017/000000039769.jpg'; const candidate_labels = ['a photo of a cat', 'a photo of a dog']; const output = await detector(url, candidate_labels); console.log(output); // [ // { score: 0.6951543688774109, label: 'a photo of a cat', box: { xmin: 326, ymin: 23, xmax: 650, ymax: 376 } }, // { score: 0.5766839385032654, label: 'a photo of a cat', box: { xmin: 6, ymin: 63, xmax: 315, ymax: 487 } } // ] ```  --- 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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
Source context: 33 downloads · 0 likes · Pipeline zero-shot-object-detection · Library transformers.js · Repo Xenova/owlv2-base-patch16-finetuned
Source context: 31 downloads · 0 likes · Pipeline zero-shot-object-detection · Library transformers.js · Repo Xenova/owlv2-base-patch16-finetuned
