Instructions to use ProbeX/Model-J__ResNet__model_idx_0671 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_0671 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_0671") 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_0671") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0671", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32a064fe2d2217775459842b7e63b714025e9c07fea8e928ae83e140612ef6db
- Size of remote file:
- 5.37 kB
- SHA256:
- ecd3a698b6ea6f414e891a2e2d414fcee3440dfa1358fb93207caba69d80e1ca
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