Instructions to use ProbeX/Model-J__ResNet__model_idx_0222 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_0222 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_0222") 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_0222") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0222") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94f748a52afe40278336e9a9d4903d118485a49dd53743f107a63b8c9f0e80e5
- Size of remote file:
- 5.37 kB
- SHA256:
- 866c39984fc64b4ffb35fcb87e5d2cb12635ab8388d99ad8e0132276cffdd56f
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