Instructions to use ProbeX/Model-J__ResNet__model_idx_0372 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0372 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0372") 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("ProbeX/Model-J__ResNet__model_idx_0372") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0372", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dbe0087212f96ac69a53dabfcef1473188445d325138e0309b7e9b64767edb5a
- Size of remote file:
- 171 MB
- SHA256:
- f6df8c02b6361654cdd70c2e9e5ba2e4008a3c939a52cda6f63e8d9d7077afb8
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