Instructions to use ProbeX/Model-J__ResNet__model_idx_0252 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_0252 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_0252") 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_0252") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0252") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 313a4b29d6a3aff294435fec8c663f06c231f9443a9023b50f0c488b87448948
- Size of remote file:
- 5.37 kB
- SHA256:
- a2edcc543f37b7df0ed4c1e18492567fbc918258c6850c317388d8cb6206ddad
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