Instructions to use ProbeX/Model-J__ResNet__model_idx_0984 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_0984 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_0984") 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_0984") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0984", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3fa00c738b946bd1f275373e45304f20db51ce3f25e2d541597ff371d176e13
- Size of remote file:
- 5.37 kB
- SHA256:
- 94bef3b2ddd48eb06033b0a1639ce463234ff3985a67ef66c0dd1913c115403c
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