Instructions to use ProbeX/Model-J__ResNet__model_idx_0693 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_0693 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_0693") 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_0693") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0693") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 905255552614ebcfc596cac2873159d8a40f49a2464171656ab3139acba3a77d
- Size of remote file:
- 5.37 kB
- SHA256:
- 155a4160f9aefa04f0328222284aebf4eb8246929b05ba2c9c6cc47f946ccefd
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