Instructions to use ProbeX/Model-J__ResNet__model_idx_0922 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_0922 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_0922") 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_0922") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0922", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 377d93bdfc03ca0322015e5be56d41500c5b3c2860a7ce0d3d75a6a6ee1e37e8
- Size of remote file:
- 171 MB
- SHA256:
- a8f97882d1835ffd0922d8437d811dfb6fb278b1aa046c27a51d79c55c9c3a27
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