Instructions to use ProbeX/Model-J__ResNet__model_idx_0065 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_0065 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_0065") 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_0065") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0065", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6cb6c461bc5570548ab71ab991b88bd3cd46c2e789938be892187fe7c658c6f1
- Size of remote file:
- 5.37 kB
- SHA256:
- 0ba29255aa05cc1f97af6c0a85694ae7e9edda1314861bfb0768c3b98b05c80c
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