Instructions to use ProbeX/Model-J__ResNet__model_idx_0501 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_0501 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_0501") 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_0501") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0501", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e556878bd50c581dcc501282dee0cc991679d4a18417b6555338df387ea801b3
- Size of remote file:
- 171 MB
- SHA256:
- 3e05625d0dc6c4502fafb90b90a52167064ae5747647385c6fed2d233217feef
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