Instructions to use ProbeX/Model-J__ResNet__model_idx_0430 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_0430 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_0430") 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_0430") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0430") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30ff1dbe4dc1ad749bcc8ba4f119f833900d06361ed8fd0ff34a88a1a92b0480
- Size of remote file:
- 171 MB
- SHA256:
- f06cf82b3fc0645048bfc9fc4c4c9384271dc37a158e10e075ffb5f887a5d936
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