ayan4m1/Watermark-Detection-YOLO11-ONNX
By ayan4m1 · ↓ 9 · ♥ 5 · Updated 2025-11-09
What's in this repo
This page lists the files in ayan4m1/Watermark-Detection-YOLO11-ONNX, including model.onnx, model_fp16.onnx.
- 2 ONNX models (.onnx files)
- 7 files, including 2 .json, 2 .onnx, 1 .md
- Last updated 2025-11-09
ONNX files
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
Watermark-Detection-YOLO11-ONNX
This is a WebGPU compatible fine-tuning of YOLO11 trained to detect watermarks.
Example
With Transformers.js:
import {
AutoModel,
AutoProcessor,
load_image
} from '@huggingface/transformers';
// require 50% confidence in watermark presence
const threshold = 0.5;
// name of this model
const modelId = 'ayan4m1/Watermark-Detection-YOLO11-ONNX';
// load it using AutoModel and AutoProcessor
const model = await AutoModel.from_pretrained(modelId, { dtype: 'fp32' });
const processor = await AutoProcessor.from_pretrained(modelId);
let watermarked = false;
// load the image and run inference
const image = await load_image(file);
const inputs = await processor(image);
const { output0 } = await model({ images: inputs.pixel_values });
// unpack the results
const permuted = output0[0].transpose(1, 0);
for (const row of permuted.tolist()) {
// data shape represents a bounding box [xCenter, yCenter, width, height, watermarkProbability]
const score = row[4];
if (score < threshold) {
continue;
}
watermarked = true;
break;
}
if (watermarked) {
...
} else {
...
}
Model info
object-detection
transformers.js
transformers.js
onnx
yolos
watermark
object-detection
dataset:bastienp/visible-watermark-pita
base_model:Ultralytics/YOLO11
base_model:finetune:Ultralytics/YOLO11
license:agpl-3.0
region:us
↓ 9
♥ 5
Created: 2025-08-13
Updated: 2025-11-09
View on Hugging FaceFiles (7)
.gitattributes
.gitignore
README.md
config.json
onnx/model.onnx
ONNX
onnx/model_fp16.onnx
ONNX
preprocessor_config.json
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