Instructions to use ProbeX/Model-J__ResNet__model_idx_0394 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_0394 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_0394") 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_0394") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0394") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7ef0c341d193febd345a5b967c1ce7eef18c64a3de356a343b28fac7fc2a8ec
- Size of remote file:
- 5.37 kB
- SHA256:
- d17378c4a3ad4b21e87ac3f312b98e575b3a5361cfbde30b08236b2fbae93caa
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