Instructions to use ProbeX/Model-J__ResNet__model_idx_0550 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_0550 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_0550") 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_0550") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0550") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 082e1312814c0bc499e5ed7eb3f00f7a9e4010bf56b9e9d25554f6240a5da9c2
- Size of remote file:
- 171 MB
- SHA256:
- b20439259ba0fb5a0b0de015dd54091077a9880f9327702e2d8863d9e8b511dc
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