Instructions to use ProbeX/Model-J__ResNet__model_idx_0434 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_0434 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_0434") 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_0434") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0434", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7c121c4b882595794af35cee59e2b3b213e166495bbcbb8210bb181cbd3718b
- Size of remote file:
- 5.37 kB
- SHA256:
- e1ec95254e463cd8f60dadbabd4cf82ad22de76b3ab5b9358220ab34cce395bb
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