Instructions to use ProbeX/Model-J__ResNet__model_idx_0196 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_0196 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_0196") 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_0196") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0196", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66b0ee81532300617b1498e05a617a0b812aae86ec2183fc9a829c2fd3f4c2b1
- Size of remote file:
- 5.37 kB
- SHA256:
- 6803f9ccd702625eab073acddeabc830247ebe87dbf53651d0b227ed694ff9a2
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