Instructions to use ProbeX/Model-J__ResNet__model_idx_0103 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_0103 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_0103") 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_0103") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0103", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 739c78c67f3f10b5a7c5c49a4b255bd807a377dda32331f9bfc8387b0bce0a85
- Size of remote file:
- 171 MB
- SHA256:
- 9831ed138106994d70f05df6aeb47b8c13c56d88b8aad54ff99668d8a6d6a30f
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