Instructions to use ProbeX/Model-J__ResNet__model_idx_0007 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_0007 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_0007") 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_0007") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0007", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66899f5429b30d99d73011a02c6ae85f49aaf21c68fddb1151fd1bd5266d2001
- Size of remote file:
- 5.37 kB
- SHA256:
- 26255a70cf6fec963a1c493f345bf9201bdd26d8fa7d485d812589891155f6b2
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