Instructions to use ProbeX/Model-J__ResNet__model_idx_0232 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_0232 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_0232") 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_0232") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0232") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7943badf581af1286da8e28d7be367b394767fd4b2633336dc29091b766fa592
- Size of remote file:
- 5.37 kB
- SHA256:
- 0ce02a61d841c22d06e986251504cc704e932b1cc097132e35b9eb26223c714d
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