onnx-community/dfine_m_coco-ONNX
By onnx-community · ↓ 0 · ♥ 0 · Updated 2025-04-30
What's in this repo
This page lists the files in onnx-community/dfine_m_coco-ONNX, including model.onnx, model_bnb4.onnx, model_fp16.onnx and more.
- 8 ONNX models (.onnx files)
- 12 files, including 8 .onnx, 2 .json, 1 .md
- Last updated 2025-04-30
ONNX files
| File | Size | Download |
|---|---|---|
| onnx/model.onnx | — | Download |
| onnx/model_bnb4.onnx | — | Download |
| onnx/model_fp16.onnx | — | Download |
| onnx/model_int8.onnx | — | Download |
| onnx/model_q4.onnx | — | Download |
| onnx/model_q4f16.onnx | — | Download |
| onnx/model_quantized.onnx | — | Download |
| onnx/model_uint8.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
https://huggingface.co/ustc-community/dfine_m_coco with ONNX weights to be compatible with Transformers.js.
Transformers.js
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @huggingface/transformers
You can then use the model like this:
import { pipeline } from "@huggingface/transformers";
const detector = await pipeline("object-detection", "onnx-community/dfine_m_coco-ONNX");
const image = "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg";
const output = await detector(image, { threshold: 0.5 });
console.log(output);
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).
Model info
Created: 2025-04-30
Updated: 2025-04-30
View on Hugging Face