Instructions to use sharmajai901/Watermark-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sharmajai901/Watermark-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sharmajai901/Watermark-detection") 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("sharmajai901/Watermark-detection") model = AutoModelForImageClassification.from_pretrained("sharmajai901/Watermark-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8da955f4503771c86722a9483e999f5f505e8d516e26d72e87c49f7e39c60ddd
- Size of remote file:
- 5.3 kB
- SHA256:
- 4138dcb234dcef0352a6fbae254152fc6e7544045ab1dbc0e451ec4366da1634
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.