Instructions to use bdsqlsz/Watermark-Detection-SigLIP2-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdsqlsz/Watermark-Detection-SigLIP2-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bdsqlsz/Watermark-Detection-SigLIP2-onnx") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bdsqlsz/Watermark-Detection-SigLIP2-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from bdsqlsz/Watermark-Detection-SigLIP2-onnx: direct link, hf CLI and curl.
- Browser
- Download file 383 Bytes
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https://huggingface.co/bdsqlsz/Watermark-Detection-SigLIP2-onnx/resolve/main/README.md
- Command line
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hf download hf://bdsqlsz/Watermark-Detection-SigLIP2-onnx/README.md
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curl -L -o README.md https://huggingface.co/bdsqlsz/Watermark-Detection-SigLIP2-onnx/resolve/main/README.md
383 Bytes
metadata
license: apache-2.0
datasets:
- qwertyforce/scenery_watermarks
language:
- en
base_model:
- google/siglip2-base-patch16-224
- prithivMLmods/Watermark-Detection-SigLIP2
pipeline_tag: image-classification
library_name: transformers
tags:
- Image-Classification
- Watermark-Detection
- SigLIP2
from https://huggingface.co/prithivMLmods/Watermark-Detection-SigLIP2