Instructions to use ProbeX/Model-J__ResNet__model_idx_0530 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_0530 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_0530") 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_0530") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0530") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b964df525188f363df84d8d0d7f04f2a4b782f50f30d249fcdee13e462306e2a
- Size of remote file:
- 5.37 kB
- SHA256:
- d9c86f41f097be26b896a3372d1024c0da1ef16c3c93fd8375a447c294935961
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