Instructions to use ProbeX/Model-J__ResNet__model_idx_0192 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_0192 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_0192") 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_0192") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0192") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cefb7265cb636f7eb8477dfc9932af3c5528d4f1fac3dbebfba429197129b8c5
- Size of remote file:
- 5.37 kB
- SHA256:
- 781138ea4fb7838aaab9cfadc986b1463d5c81852e79c145078ec2ce62a26921
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