Instructions to use ProbeX/Model-J__ResNet__model_idx_0375 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_0375 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_0375") 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_0375") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0375", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94f787fa89fb8d4a9c7f0cf235a19b54525b0324cd94358a4b5bb7dcea861b72
- Size of remote file:
- 5.37 kB
- SHA256:
- dc74327fe5a1c9604835fe5e17659adf76a3d95305ae9f46201a1fd10f1dabe8
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