Instructions to use ProbeX/Model-J__ResNet__model_idx_0819 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_0819 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_0819") 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_0819") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0819") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 660ad80b9cf8d8a362738b44456ef4c39378a0917dab72bed281c0eab06b0226
- Size of remote file:
- 5.37 kB
- SHA256:
- d8fe7edf3c17bd12a347fd2c461efbe0b7d1648378cd240dbc8a8bc79107e5f6
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