Instructions to use ProbeX/Model-J__ResNet__model_idx_0685 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_0685 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_0685") 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_0685") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0685", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ecc87028b2562f25a111a6e4d7e40a6dadd03ecedaad35448eac005427d8910
- Size of remote file:
- 5.37 kB
- SHA256:
- b791f2d536153bf295a7c821ca6a7d2ad9d094688b1f5bac683171cd4b854bc2
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