Instructions to use ProbeX/Model-J__ResNet__model_idx_0179 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_0179 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_0179") 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_0179") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0179", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f1ad31f1463090c85fef8a75563b5a09ba37233dcbbe564d1f8862b1f04c2ac
- Size of remote file:
- 171 MB
- SHA256:
- ca7951d4aeffaa6a96ed6cbaf191a75f65401f2e12119a72bcd569452a8b5e76
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