Instructions to use ProbeX/Model-J__ResNet__model_idx_0905 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_0905 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_0905") 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_0905") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0905", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e291a86b768ec82995afc8ffcd808950c074045491c5cc0a36e42cc97181ca8
- Size of remote file:
- 171 MB
- SHA256:
- 1b8cc8cfac3b9c1f03bdfbe128e4dbcfe34d8c228cb1613cbbbcc43b88fe1818
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