Instructions to use ProbeX/Model-J__ResNet__model_idx_0877 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_0877 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_0877") 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_0877") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0877", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de19e25589f20f4fedbaf4cbc8be80ac84c7f629b3de268cd31bbf44492fbd99
- Size of remote file:
- 5.37 kB
- SHA256:
- 03686f6b408e089f3715a006a47e1034178f023fd291b21a113d697dd1022ea9
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