Instructions to use ProbeX/Model-J__ResNet__model_idx_0583 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_0583 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_0583") 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_0583") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0583") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70454b1fc4e15eb80dbecb6ed73efcb731dcfd83ac88f5bcd0162d84509b5fb3
- Size of remote file:
- 5.37 kB
- SHA256:
- f02ce90a1484f1af156ecfa6edff7fee72504800d802ce898e135342ab7aae9e
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