Instructions to use ProbeX/Model-J__ResNet__model_idx_0429 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_0429 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_0429") 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_0429") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0429", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93899b7cd5eed86d08f4c4ccc8ae38c8624639988cbe7d723d2f9a307feb8fde
- Size of remote file:
- 5.37 kB
- SHA256:
- c2837864cca0c26c0ab8b1776a2c58adf11ab73af9981babf2d129788fed6c00
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