Instructions to use ProbeX/Model-J__ResNet__model_idx_0480 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_0480 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_0480") 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_0480") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0480") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4495641ff125100fa1011c1f4386df743f58d9123756ac1f8b2d609ab4d19923
- Size of remote file:
- 5.37 kB
- SHA256:
- 408e1e2ea1b77ec5c8592acbcc3219196bf6aa15acc923a3b0d27e27bb8cbfa7
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