Instructions to use ProbeX/Model-J__ResNet__model_idx_0436 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_0436 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_0436") 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_0436") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0436") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 873be6b32084158f4665097e1e6ba5f5245f5d5d5ad888f3698fa80b2bd1019a
- Size of remote file:
- 5.37 kB
- SHA256:
- 132561bc89b2253bb3827ad05e4cc66ea146514eba167fa988eab005458f91ff
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