Instructions to use ProbeX/Model-J__ResNet__model_idx_0440 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_0440 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_0440") 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_0440") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0440", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63da043b4d5d16b719e3f88a0c3e4e0146e7ff376545aa9a083ba2dfc55e02e1
- Size of remote file:
- 5.37 kB
- SHA256:
- 263c67ee2cf6de2f3f5930660af766835b6c595c78304e075205aaa4f873de53
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