Instructions to use ProbeX/Model-J__ResNet__model_idx_0067 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_0067 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_0067") 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_0067") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0067") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad1b72a0c8d1823b7fdfd675bcf0a1974de950ed8fe3c12a64deecd670b0d875
- Size of remote file:
- 5.37 kB
- SHA256:
- 194cbd0ef50957b58479a66a432285388a5525afd2c3d63c9797c45d1fea9cb3
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