Instructions to use ProbeX/Model-J__ResNet__model_idx_0289 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_0289 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_0289") 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_0289") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0289") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47dceb9a41fb3a54f9b54eb253439bbf64cadc0f7952f6764e4717adb2ee5280
- Size of remote file:
- 5.37 kB
- SHA256:
- 1310f013d6750854934e5f0da058bdd2e4398a28845e5bf4369c8b9b335ab401
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