Instructions to use ProbeX/Model-J__ResNet__model_idx_0185 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_0185 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_0185") 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_0185") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0185") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8569efe16b537beb3987b137b2212f86e81c16c026fc4a44946cca935ddf706b
- Size of remote file:
- 5.37 kB
- SHA256:
- dfe4e9fa8d0c5aa472f4c7f157de8db12761fa113b4406378118fead3dfaca9c
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