Instructions to use ProbeX/Model-J__ResNet__model_idx_0946 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_0946 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_0946") 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_0946") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0946", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a462782830f49994a488744dcc319b76533853ce3292fafdb18a4a8f609d98ac
- Size of remote file:
- 5.37 kB
- SHA256:
- 42042cf5b9f153704269ac21fd04a34f3ed72b20bcfa69c37cc9f11e238d8a1b
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