Instructions to use ProbeX/Model-J__ResNet__model_idx_0898 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_0898 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_0898") 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_0898") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0898") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e2f46cf94316cc2f4340d07392562f9cc9daaeefc4a4afe1ecde07c66206587
- Size of remote file:
- 5.37 kB
- SHA256:
- 84b1084ea47dd3f083470abecb218a5a8f08d24ad5abec6bc030c5bd779089ae
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