Owlvit Base Patch32
with ONNX weights to be compatible with Transformers.js.
Modellquelle
- Art
- Other
- Dateigröße
- 121–584 MB
- Basismodell
- google/owlvit-base-patch32
- Version
- vRevision b75f4e5
- Aktualisiert
- 10. Okt. 2026
- Downloads
- 15.698
- Quelle
- Hugging Face
Quellenbeschreibung
https://huggingface.co/google/owlvit-base-patch32 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/owlvit-base-patch32.
import { pipeline } from '@huggingface/transformers';
const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/astronaut.png';
const candidate_labels = ['human face', 'rocket', 'helmet', 'american flag'];
const output = await detector(url, candidate_labels);
// [
// { score: 0.24392342567443848, label: 'human face', box: { xmin: 180, ymin: 67, xmax: 274, ymax: 175 } },
// { score: 0.15129457414150238, label: 'american flag', box: { xmin: 0, ymin: 4, xmax: 106, ymax: 513 } },
// { score: 0.13649864494800568, label: 'helmet', box: { xmin: 277, ymin: 337, xmax: 511, ymax: 511 } },
// { score: 0.10262022167444229, label: 'rocket', box: { xmin: 352, ymin: -1, xmax: 463, ymax: 287 } }
// ]
image/png
Example: Zero-shot object detection w/ Xenova/owlvit-base-patch32 (additional parameters).
import { pipeline } from '@huggingface/transformers';
const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/beach.png';
const candidate_labels = ['hat', 'book', 'sunglasses', 'camera'];
const output = await detector(url, candidate_labels, { top_k: 4, threshold: 0.05 });
// [
// { score: 0.1606510728597641, label: 'sunglasses', box: { xmin: 347, ymin: 229, xmax: 429, ymax: 264 } },
// { score: 0.08935828506946564, label: 'hat', box: { xmin: 38, ymin: 174, xmax: 258, ymax: 364 } },
// { score: 0.08530698716640472, label: 'camera', box: { xmin: 187, ymin: 350, xmax: 260, ymax: 411 } },
// { score: 0.08349756896495819, label: 'book', box: { xmin: 261, ymin: 280, xmax: 494, ymax: 425 } }
// ]
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).
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 10. Okt.
Modellartefakte
7 Artefakte
Modellartefakte
7 ArtefakteQuellenauszüge
3 Auszüge
Quellenauszüge
3 Auszüge--- base_model: google/owlvit-base-patch32 library_name: transformers.js pipeline_tag: zero-shot-object-detection --- https://huggingface.co/google/owlvit-base-patch32 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/owlvit-base-patch32`. ```js import { pipeline } from '@huggingface/transformers'; const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32'); const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/astronaut.png'; const candidate_labels = ['human face', 'rocket', 'helmet', 'american flag']; const output = await detector(url, candidate_labels); // [ // { score: 0.24392342567443848, label: 'human face', box: { xmin: 180, ymin: 67, xmax: 274, ymax: 175 } }, // { score: 0.15129457414150238, label: 'american flag', box: { xmin: 0, ymin: 4, xmax: 106, ymax: 513 } }, // { score: 0.13649864494800568, label: 'helmet', box: { xmin: 277, ymin: 337, xmax: 511, ymax: 511 } }, // { score: 0.10262022167444229, label: 'rocket', box: { xmin: 352, ymin: -1, xmax: 463, ymax: 287 } } // ] ```  **Example:** Zero-shot object detection w/ `Xenova/owlvit-base-patch32` (additional parameters). ```js import { pipeline } from '@huggingface/transformers'; const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32'); const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/beach.png'; const candidate_labels = ['hat', 'book', 'sunglasses', 'camera']; const output = await detector(url, candidate_labels, { top_k: 4, threshold: 0.05 }); // [ // { score: 0.1606510728597641, label: 'sunglasses', box: { xmin: 347, ymin: 229, xmax: 429, ymax: 264 } }, // { score: 0.08935828506946564, label: 'hat', box: { xmin: 38, ymin: 174, xmax: 258, ymax: 364 } }, // { score: 0.08530698716640472, label: 'camera', box: { xmin: 187, ymin: 350, xmax: 260, ymax: 411 } }, // { score: 0.08349756896495819, label: 'book', box: {...
Source context: 15698 downloads · 1 likes · Pipeline zero-shot-object-detection · Library transformers.js · Repo Xenova/owlvit-base-patch32
Source context: 1845 downloads · 1 likes · Pipeline zero-shot-object-detection · Library transformers.js · Repo Xenova/owlvit-base-patch32
