Instructions to use ProbeX/Model-J__ResNet__model_idx_0604 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_0604 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_0604") 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_0604") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0604", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f127b4f9774a5d912db4d6f413191f75ed3ad8c0b93ad19dbde0af5bd2e85f4f
- Size of remote file:
- 171 MB
- SHA256:
- 035b24840f73b9fb5803ae1ff40bf5df447418f18b27eaf27e560e002261c99d
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