Instructions to use ProbeX/Model-J__ResNet__model_idx_0450 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_0450 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_0450") 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_0450") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0450") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7e3c099b975409a656088a0bfda44e70dd36b83b64a817a9868a971a765b7d1
- Size of remote file:
- 5.37 kB
- SHA256:
- 99d7a93f82a6b26d36fa7429c5b8cea6845065e9a1f587843f3c79b659a01881
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