Instructions to use ProbeX/Model-J__ResNet__model_idx_0863 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_0863 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_0863") 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_0863") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0863", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 992d932aefd3ec4953e5f2cf2bf2f8e0c1f8d7dbd347b704d5d4c62c40f37b1f
- Size of remote file:
- 171 MB
- SHA256:
- 13efe0250912ae1f5051bb765dea6e3b5877c95d713ac7bf2d216c77172dcf13
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