Instructions to use ProbeX/Model-J__ResNet__model_idx_0500 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_0500 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_0500") 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_0500") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0500", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bff14235f0b3f4c091378ce1062feaa7c7bb16654cd932dfd86e2e16b771955
- Size of remote file:
- 5.37 kB
- SHA256:
- 9f4ca3f38b50df2d022b38b7e076cbb90d63d98f3759fa997cadb71fdfe8d650
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