Instructions to use ProbeX/Model-J__ResNet__model_idx_0113 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_0113 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_0113") 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_0113") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0113") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6cb952e57e2260425f339850a09004a3c08ce2f6310e34b88e219750d6cbee39
- Size of remote file:
- 171 MB
- SHA256:
- 226e60aa131b47f6b5b899641025d439a476308bda27454f2e4e32b173b747d4
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