Instructions to use ProbeX/Model-J__ResNet__model_idx_0041 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_0041 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_0041") 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_0041") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0041") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 710e15d19374295d3faf3d2038c88a6096a6efaa1ecb9e9be08e35151d4b878b
- Size of remote file:
- 5.37 kB
- SHA256:
- 7851392d3a85286efa49eac5bd3fbd31098c500a4673990732d4b9dd44e99e73
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