Instructions to use ProbeX/Model-J__ResNet__model_idx_0040 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_0040 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_0040") 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_0040") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0040", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6442c02a14e59943e3c674ce35b4c98dfdcbe9fa8f5083b0c472c3467403eeb1
- Size of remote file:
- 5.37 kB
- SHA256:
- 2f0d9f8a16b5e944e02238e6113ae8dd04a24c99f488b4ac5f213b25b35d275e
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