Instructions to use ProbeX/Model-J__ResNet__model_idx_0856 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_0856 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_0856") 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_0856") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0856") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4957d37a8d1ae52abde31b370789e766ddb0cbeeefcadf9630a2046ab89a8171
- Size of remote file:
- 5.37 kB
- SHA256:
- c9d6cf465b79af643317929692d99d0abc2b08a3672138e6b3ee82789bac66f3
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