Instructions to use ProbeX/Model-J__ResNet__model_idx_0853 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_0853 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_0853") 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_0853") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0853") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d3c348d059465d544aaf085aafdcfd1cbea63b9d8a7c60a29fda3d34a4d961a
- Size of remote file:
- 5.37 kB
- SHA256:
- 714e37ba3df1bf50bf468a49000966ad60f9efb6de48a4b88af526fb7c59ccf4
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