Instructions to use ProbeX/Model-J__ResNet__model_idx_0293 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_0293 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_0293") 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_0293") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0293", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbe889b447a04c1b46c145d1ae9a83bddfd4dcdeb1e62e1612640b004e78e57d
- Size of remote file:
- 171 MB
- SHA256:
- 570b4e8ba65e0a8f3c55c14d344562cac709d47fb9c365a7f23202a7b9179b91
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