Instructions to use ProbeX/Model-J__ResNet__model_idx_0623 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_0623 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_0623") 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_0623") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0623", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c394ae816834a45d5573e17e93d222082b4fa24c07235edf3d8272e6337f50a
- Size of remote file:
- 5.37 kB
- SHA256:
- 17b1a13b58f3c547464a6b473724da1f451fcd10018dcc17adf6112952674bb6
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