Instructions to use ProbeX/Model-J__ResNet__model_idx_0286 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_0286 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_0286") 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_0286") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0286") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56f5ffdf7bdd3aa2feb7dab31e3bee4215e21826a5c4a7d33e48149882135bb2
- Size of remote file:
- 5.37 kB
- SHA256:
- 8232ad0efee16a2849fe2ff0fc84f7c7f1041a94feb39364ba439a1b7e1dde2f
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