Instructions to use ProbeX/Model-J__ResNet__model_idx_0656 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_0656 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_0656") 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_0656") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0656", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f58710fc3eaceed09752e442f873bf3221a56773a6f71ef50c9d0cba0ac52f43
- Size of remote file:
- 5.37 kB
- SHA256:
- cde77731a2de2c34880ef1548f9001ee8d43827775920e9bd5f271b37bce884c
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