Instructions to use dhhd255/EfficientNet_ParkinsonsPred with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dhhd255/EfficientNet_ParkinsonsPred with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="dhhd255/EfficientNet_ParkinsonsPred")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("dhhd255/EfficientNet_ParkinsonsPred") model = AutoModel.from_pretrained("dhhd255/EfficientNet_ParkinsonsPred", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ccbbcb963e725ca3cd4f73234107cf8452b6680a9fc7c18539e3dccf711023a6
- Size of remote file:
- 257 MB
- SHA256:
- 8ff2d278e68bb09c7bb9d15f071af0b4abd6f1850f7504595030a0a49d4d0e8b
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