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:
- ee6d2f118932b0e9f59ecd7e45c9b23853ef01b3c85386236de81d73057c8114
- Size of remote file:
- 343 MB
- SHA256:
- ae1f3f0da1454b92763e6fbcc02d1365df7df4843ae939c6ce93fe8f97acaa61
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