Image Classification
Transformers
Safetensors
English
siglip
Image-Classification
Watermark-Detection
SigLIP2
Instructions to use Rishabh5150/Watermark-Detection-SigLIP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rishabh5150/Watermark-Detection-SigLIP2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Rishabh5150/Watermark-Detection-SigLIP2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("Rishabh5150/Watermark-Detection-SigLIP2") model = AutoModelForImageClassification.from_pretrained("Rishabh5150/Watermark-Detection-SigLIP2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 302f8c23b71f36568726bf70a3582d2afcd3163604b8d6e97a676fbb8994d89a
- Size of remote file:
- 372 MB
- SHA256:
- c0cbfb77eb98b584a4f5d3aabe2ad6d96b546958cd2d9a0587f4c9793b4a42ff
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