Instructions to use ProbeX/Model-J__ResNet__model_idx_0013 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_0013 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_0013") 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_0013") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0013", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f70b258fb7c9bd0e539a79c6b471836f97bef9aface901259904b87aa370cbd2
- Size of remote file:
- 5.37 kB
- SHA256:
- 03a0611bb6cc8be57aaac360a3e36994c1265cc216da090017dc1bdc442adf41
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