Instructions to use ProbeX/Model-J__ResNet__model_idx_0739 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_0739 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_0739") 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_0739") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0739") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8a67482ee9a8bc2dbbe18e6c4efecc905296c740e7e544d119fb33b51753b72
- Size of remote file:
- 5.37 kB
- SHA256:
- 2aabdcb54ed2319ed53250147c9c6aa362647d1943196c5fc3532f8954f117fd
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