Instructions to use ProbeX/Model-J__ResNet__model_idx_0519 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_0519 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_0519") 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_0519") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0519") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26ee46d8f55e62b9eb897f655d71eeae8661087936126711281d558302fac889
- Size of remote file:
- 5.37 kB
- SHA256:
- 0d45d9bfe008bca403d01b211e7d5f8d04c935d025731ee731d492e1bab14b22
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