Instructions to use ProbeX/Model-J__ResNet__model_idx_0993 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_0993 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_0993") 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_0993") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0993") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eaa858c747b4aeee23aa5567f620338c93e5709f5e75f1b2cb02592bc9491106
- Size of remote file:
- 5.37 kB
- SHA256:
- 56c358a0d897cb5b7d24f8d24abc4bb9ee6a27618f6c335af4d7a224b48fdbd4
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