Instructions to use ProbeX/Model-J__ResNet__model_idx_0165 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_0165 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_0165") 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_0165") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0165", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53b5bf3e57927d557725eb62289431b1a6ab97847ef4aea788edaef1867fb873
- Size of remote file:
- 171 MB
- SHA256:
- 5394c5236f6e6220accd7eba7ff18a10abf606838e1e50ec42d964141fc80eb0
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