Instructions to use TimKond/diffusion-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TimKond/diffusion-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="TimKond/diffusion-detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("TimKond/diffusion-detection") model = AutoModelForImageClassification.from_pretrained("TimKond/diffusion-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d87accd0856ff59a9f81940a9229b8b0af19775db509935db4f78e9c16e2603
- Size of remote file:
- 3.89 kB
- SHA256:
- be8111d37711672abda6e625db4e546a72df20e34a5202612241de16cbfe4135
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.