Instructions to use ProbeX/Model-J__ResNet__model_idx_0726 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_0726 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_0726") 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_0726") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0726") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bff95d031b2309559b415c5d3900dbc6fc4ad53df6712b9734363b2fd73d315
- Size of remote file:
- 5.37 kB
- SHA256:
- bc064cf34a11351ae61e92848cfac1c8a20ea0ff1d7db487bff3bd9f992eede1
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