Instructions to use ProbeX/Model-J__ResNet__model_idx_0818 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_0818 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_0818") 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_0818") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0818") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07c57bd0b2d937f9db871da8a072de209f2cf91c2a8cffab3b9039c10b6fcf86
- Size of remote file:
- 5.37 kB
- SHA256:
- fa665c4fe5071bf49c264187970d98270481772198458add0d9ee09c3616754c
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