Instructions to use ProbeX/Model-J__ResNet__model_idx_0383 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_0383 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_0383", device_map="auto") 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_0383") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0383", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd19aae098da155f582fc027e65cdcf820d8fa0d4a359759b81267e4785e1f82
- Size of remote file:
- 5.37 kB
- SHA256:
- b6bdd0b741de80f88473fa3fc5f68a0e1b814616bfee5ff98cb8c068994872f5
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