Instructions to use ProbeX/Model-J__ResNet__model_idx_0020 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_0020 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_0020") 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_0020") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0020") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48d8a787eb8d77f94f61f66867f4ed560e6ecea24ebd5429a20eba789c486485
- Size of remote file:
- 5.37 kB
- SHA256:
- 34d710d6ae84bc57011773e353f9f14aa1c77963f6f57ae5ee5576713b5c63ee
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