Instructions to use ProbeX/Model-J__ResNet__model_idx_0309 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_0309 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_0309") 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_0309") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0309") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1eb892d328f36992f49696c9bfcbb0c3ca40cefd88efb0896fda6e1769408a8b
- Size of remote file:
- 5.37 kB
- SHA256:
- 6a6bb3fc8ff991d16175481d4e2d43664077cc453a9dd857efc94cecf33864de
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