Instructions to use ProbeX/Model-J__ResNet__model_idx_0801 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_0801 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_0801") 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_0801") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0801", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4fc6f8dec318d1cc62090b394bc62d468273a663752b4e98610c63033ea704bc
- Size of remote file:
- 5.37 kB
- SHA256:
- 0625cd0907ac2d5554ee86353a1c2c3992043c92a6f4e07a3f31c590ecfa113a
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