Instructions to use ProbeX/Model-J__ResNet__model_idx_0804 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_0804 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_0804") 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_0804") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0804") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9bf2312b7d586276ecb37af4a05a4950f5d61eca5e2d14363837501945a73285
- Size of remote file:
- 171 MB
- SHA256:
- 39dc3c36080a46ad3013d9f41c9f7ba14fbdfc452ec4387d12f5b837b8eae4f8
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