Instructions to use ProbeX/Model-J__ResNet__model_idx_0823 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_0823 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_0823") 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_0823") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0823") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f533de331e13c72aa5b6b6b8c43220b88760128626ecd4ea0156b0dd86afc7e7
- Size of remote file:
- 5.37 kB
- SHA256:
- 03f7d4ba7e55d21172cf54670f77fda0faddd044ee730c24e41aa3619f7afe4d
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