Instructions to use ProbeX/Model-J__ResNet__model_idx_0952 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_0952 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_0952") 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_0952") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0952", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- caad16f9dd9bbf4d751290385badf58b7be1d36fd6525ad6e022e8fcc595dab9
- Size of remote file:
- 171 MB
- SHA256:
- 2286ba5c92055692b2b11d1f4b0e7ef680ed39a2dae1026957dd226e546a6de6
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