Instructions to use ProbeX/Model-J__ResNet__model_idx_1001 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_1001 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_1001") 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_1001") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_1001") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fefc8e397788afc6ad77a52bbe01d9669318516848babf48f6c96c66963d283d
- Size of remote file:
- 5.37 kB
- SHA256:
- f655d03cdc990aea3a7e01219410b343d4912a502bf711e603c3837efef60946
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