Instructions to use ProbeX/Model-J__ResNet__model_idx_0247 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_0247 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_0247") 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_0247") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0247") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8506fdaad2be1f4aeb8e024cdb0a2d15373284f12156a401d88e2440916ef623
- Size of remote file:
- 5.37 kB
- SHA256:
- 5cf88769400e58867361ca4641050cb915bbb087baf1edb6a299ef247a10edc4
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