Instructions to use ProbeX/Model-J__ResNet__model_idx_0839 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_0839 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_0839") 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_0839") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0839", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0652e89964c22cbd9e4996df4b8f45ed9912d5727b9e590035d4da9c3d1c8758
- Size of remote file:
- 171 MB
- SHA256:
- ffcb492e1b1b5c2a5f5da5c7700335dc7c1862bec4671f7603b31f4422182ef3
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