Instructions to use ProbeX/Model-J__ResNet__model_idx_0091 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_0091 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_0091") 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_0091") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0091") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e5f2ee89899d400c643975a633eaecc9f58d64a27c54f157b11600cc02761c1
- Size of remote file:
- 5.37 kB
- SHA256:
- 496bd9059b96ddb4a1dab33833d4a3f192eb93af459a302139b2abf39147c41d
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