Instructions to use ProbeX/Model-J__ResNet__model_idx_0336 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_0336 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_0336") 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_0336") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0336") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d948be1d89a0e3ca038d5577e32ac9eacaf41ddae96326a8ca7d9c44e5a090b
- Size of remote file:
- 5.37 kB
- SHA256:
- 3202be3273f2c453cb4601771b80a4d0259fe202e20dad37363eff5077272b33
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