Instructions to use ProbeX/Model-J__ResNet__model_idx_0440 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_0440 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_0440") 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_0440") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0440", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c61172315137f6549f378a0bf8aa9994f97b05a8bc94daf63429b8f13cca53a3
- Size of remote file:
- 171 MB
- SHA256:
- 5e033bdae9e1ff808fd5a91882299d01fa681a900d16e3e54008f1280655acc2
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