Instructions to use vm24/dfm_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vm24/dfm_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="vm24/dfm_model") 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("vm24/dfm_model") model = AutoModelForImageClassification.from_pretrained("vm24/dfm_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4719533a0c6253dde499cb26420eb858a3ade8ebf4fa4264ac527053aec6150d
- Size of remote file:
- 4.6 kB
- SHA256:
- f484c0c119950cdacad5f1cbd2aac30d22ab021a11bc6242cc3e2cb5c108c64d
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