Instructions to use ProbeX/Model-J__ResNet__model_idx_0301 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_0301 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_0301") 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_0301") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0301", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 219c07b6833ee7924721c2a16903e3016b80d460bd4f05f191b3ddc2472685c1
- Size of remote file:
- 171 MB
- SHA256:
- a957f38752f6138eac4c83275e98441768b1838bd2acc2db629008235ccd4c96
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