Instructions to use ProbeX/Model-J__ResNet__model_idx_0639 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_0639 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_0639") 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_0639") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0639", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b856dfd9f70e79bba8bc55c1b3fdc59fe5f35a552cc747ecad38fd057d4e325
- Size of remote file:
- 171 MB
- SHA256:
- 91d658cef6f90b5d50c74a61511ba048a7370610a1bbd1de6fda2e2b09be98d3
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