Instructions to use ProbeX/Model-J__ResNet__model_idx_0975 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_0975 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_0975") 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_0975") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0975") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44d335bafd2ed7a9fcfdb7e6c40c8bbbce10d5a12caf8843be53cfa591e80bea
- Size of remote file:
- 5.37 kB
- SHA256:
- a54c328eaf670aab8984fdb58f6f4735a6a6e9f2d5aa1cfc2aeb6d26473f6f18
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