Instructions to use ProbeX/Model-J__ResNet__model_idx_0034 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_0034 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_0034") 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_0034") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0034") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cdde54434ab1a13ba7f1c0a8532e4721abc3b3598a340cb7a836a01f614a1b26
- Size of remote file:
- 5.37 kB
- SHA256:
- efa72e39aeadf0de152a24fc2e7d1b3e463eed18aa52544163e59e4a65c1d8ae
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