Instructions to use ProbeX/Model-J__ResNet__model_idx_0733 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_0733 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_0733") 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_0733") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0733", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5bc637a2cf7554f5edfaea189327c27664a71a10906af6c68a0bb06e381c1316
- Size of remote file:
- 5.37 kB
- SHA256:
- 2d20ba0efac103a234e16ddffb816eb18abc2651e52b1a05f54d5d796c9fdfcb
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