Instructions to use ProbeX/Model-J__ResNet__model_idx_0182 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_0182 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_0182") 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_0182") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0182", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed473b89f3dde31a6cc97f37c8f3ca7efc41078b54b7d96fe3619f3ff8b16b3c
- Size of remote file:
- 171 MB
- SHA256:
- a7d4825c423facbb22f399bb6544976b97ad27a72d60ef17495ff47598609c53
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