Instructions to use ProbeX/Model-J__ResNet__model_idx_0219 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_0219 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_0219") 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_0219") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0219", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13daf2b7a9b1b3651e7e6c97ed63937b9f41d347367e0aa8e4431129e12911d9
- Size of remote file:
- 171 MB
- SHA256:
- 82bf78d6d5a276f7feeda1037d5e70725ee36d5168e36a27a9fa36ac656ee866
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