Instructions to use ProbeX/Model-J__ResNet__model_idx_0899 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_0899 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_0899") 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_0899") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0899") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7beb097848f47d9a30f116363c8d5dfb55e97a7bc65669c3ee86bc4ef5107514
- Size of remote file:
- 5.37 kB
- SHA256:
- cd3ab5aded7d34122f227036642dc932127525d53e53b9306e06d22bcc473dd5
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