Instructions to use ProbeX/Model-J__ResNet__model_idx_0560 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_0560 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_0560") 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_0560") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0560") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ec4ca56f97d6c05a0c43420dc1fcf8c27cd04885489c27d1fb58537c90fd020
- Size of remote file:
- 171 MB
- SHA256:
- 853025352a1fabbc42381a57f6901545fba7722c8777c70f0c94af2698a886f0
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