Instructions to use ProbeX/Model-J__ResNet__model_idx_0362 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_0362 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_0362") 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_0362") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0362") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f3dee72737e86ffb530c13368cae89449a2c925059331f3f0e3178deb0a761b2
- Size of remote file:
- 5.37 kB
- SHA256:
- 107bfb47816a3e6bcf6ff51643f88ca3aef7960b53fbf0a2af8509796271c564
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