Instructions to use ProbeX/Model-J__ResNet__model_idx_0419 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_0419 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_0419") 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_0419") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0419") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b42c493f5eb325f45291f0973b283334633c4a14daae53a98e7cde584cdba73d
- Size of remote file:
- 5.37 kB
- SHA256:
- ec6cab1c532cc1ad76bf9fe107a482af913e2af6dee27b8fecfa963db6283fe0
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