Instructions to use ProbeX/Model-J__ResNet__model_idx_0115 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_0115 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_0115", device_map="auto") 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_0115") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0115", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 628c835e53ddd9d6a2af91ed7b0ede2fefbd589bdc49db6e1abcb8c845543a99
- Size of remote file:
- 5.37 kB
- SHA256:
- ccadceda2cd04d45161968deb13f38901335ca4437eecff2de2ec664d296d1db
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