Instructions to use ProbeX/Model-J__ResNet__model_idx_0033 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_0033 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_0033") 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_0033") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0033", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- caec63865da4af9277c79916fce82df987ccd38f5374dbe22242635b9d8f6642
- Size of remote file:
- 5.37 kB
- SHA256:
- e667e00bf904652b21c12d99fbfb5d8275eea4e9b705f132fc74b31fb892d0e7
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