Instructions to use ProbeX/Model-J__ResNet__model_idx_0318 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_0318 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_0318") 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_0318") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0318", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 095333118459ced39ab9ad17a382db875a81468e034e1e7a98e0b38f32cf4d01
- Size of remote file:
- 5.37 kB
- SHA256:
- a32303e3998732db6331b7851444dee917193074dde250e06d61992292f0625d
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