Instructions to use ProbeX/Model-J__ResNet__model_idx_0138 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_0138 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_0138") 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_0138") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0138", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 85877b531b4b01a8957cffeaa2dce4627ee407bc02cb982da1b46011496538c5
- Size of remote file:
- 5.37 kB
- SHA256:
- 288ca91591f25a6d9b02c209136c10fee47461f44471889f8dfc23a43c69fec8
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