Instructions to use ProbeX/Model-J__ResNet__model_idx_0668 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_0668 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_0668") 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_0668") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0668") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f84785f79444082139eac44340a3da70a866860cb11e18594de9a478d17122c4
- Size of remote file:
- 5.37 kB
- SHA256:
- 12bbf76a774b4255f1324055c35cffd7b987a8928da1e51043e9469080c51737
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