Instructions to use ProbeX/Model-J__ResNet__model_idx_0174 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_0174 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_0174") 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_0174") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0174") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38c2a8e37b24e613e83e1fbdf2f8b96809447ddc023d36556b959d0db9eb8260
- Size of remote file:
- 5.37 kB
- SHA256:
- 4ac89cd3ee0250ad9e783b10f93bd64e4c7e13b24936847c777b3a15bb01e350
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