Instructions to use ProbeX/Model-J__ResNet__model_idx_0277 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_0277 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_0277") 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_0277") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0277") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f0180a65fececdaabb7fb351dc2bb8cb74ed25e05c8b18559df670976c993d9
- Size of remote file:
- 5.37 kB
- SHA256:
- 5d0660add34e6c64c5775d2bea1edff8092f7827843aa0d5623802bb5d54e71e
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