Instructions to use ProbeX/Model-J__ResNet__model_idx_0120 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_0120 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_0120") 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_0120") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0120") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77d1fbcf98d8bffb6c05c03830dd6b38df7df7298df2078c03d6de500a0ae507
- Size of remote file:
- 5.37 kB
- SHA256:
- 25a503e856834c8b5ab54f0dbec472d357a5e0953224e64e37bff22c1adfb76d
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