Instructions to use ProbeX/Model-J__ResNet__model_idx_0863 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_0863 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_0863") 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_0863") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0863", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a3cf63ae54a6f0f0a673c35bef75c92e747169c3213e9169567e198870e5f86
- Size of remote file:
- 5.37 kB
- SHA256:
- 3505819e6e3c612cf8c72d20c0b52eb6e1d041dc13c73eeae10a39918063d619
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