Instructions to use ProbeX/Model-J__ResNet__model_idx_0194 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_0194 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_0194") 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_0194") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0194") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab68b6d1ff14087372f4dee6b08e38ebb50d065393d746d74215549bf828d5c2
- Size of remote file:
- 5.37 kB
- SHA256:
- 482ef39882c171cc7e9ae7510ce9533abad901e9e4ca2d41f196e84707d43cf5
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