Instructions to use ProbeX/Model-J__ResNet__model_idx_0760 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_0760 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_0760") 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_0760") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0760") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34b8e3fcf16c09ab437d97777019a68be8b8bc439344527e21f15d67d2bbf7f1
- Size of remote file:
- 5.37 kB
- SHA256:
- 822f237cdf5d1d67bdb1b10c03896d23c842f9f476c8737ef627cd19d3c4f4ae
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