Instructions to use ProbeX/Model-J__ResNet__model_idx_0189 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_0189 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_0189") 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_0189") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0189", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f3b88a61c0844fbd971322207b8bde5248a4a6be5a641b2b0c0725ba29a573b
- Size of remote file:
- 171 MB
- SHA256:
- 04ce5b36bc168169259fa1d8547aebe5a296dfe03df0fd59d0fefafc3fc6eb77
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