Instructions to use ProbeX/Model-J__ResNet__model_idx_0317 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_0317 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_0317") 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_0317") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0317") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8f2917687f55c08762cc91b379ff3a54940f5216130f5b78733ed7d4a35580a
- Size of remote file:
- 5.37 kB
- SHA256:
- 4c193c053b4957b99825c24009b9cae7d5dc914836dcbcbf3d62f08c7782fcc9
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