Instructions to use ProbeX/Model-J__ResNet__model_idx_0163 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_0163 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_0163") 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_0163") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0163", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 266cfdb144d08994b2428e3252c5b63f2d9e14c0aa5c001046783b0d5da10839
- Size of remote file:
- 5.37 kB
- SHA256:
- 35de3cc8b16ff93f3991c7e109b3913c3a1a7f8985ec8d3e0ae110250b95f337
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