Instructions to use ProbeX/Model-J__ResNet__model_idx_0429 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_0429 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_0429") 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_0429") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0429", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f284c1db6af096c9c52dc01a4d8b51b98472d3d3979c20b99c7ea4ad0f18591
- Size of remote file:
- 171 MB
- SHA256:
- 4a595f6159340d0d9332f42bfffaceacae456a9a9f67b2f18236127710075ce1
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