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:
- e6df85a700b1dce97fb34ef08ad7e8fbe41d2fd94b3e81b291aa7360fe529940
- Size of remote file:
- 171 MB
- SHA256:
- ed75ce62c9f875821e44b588cff57c41ac8a74df8b63593720af300036826f70
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