Instructions to use ProbeX/Model-J__ResNet__model_idx_0164 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_0164 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_0164") 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_0164") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0164", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1463d99c697484b149816f410d7e5bce5a9269f08254a2f7cad4a25df4515c4a
- Size of remote file:
- 5.37 kB
- SHA256:
- f8ff1f98786986468d56986a16f8a8ac52ccf22cc7672a76690995118f6c0c1c
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