Instructions to use ProbeX/Model-J__ResNet__model_idx_0518 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_0518 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_0518") 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_0518") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0518") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff949dfe7fcb95672370dba33ff36224417244cdae6b97f5865d6401c701e810
- Size of remote file:
- 5.37 kB
- SHA256:
- 84fb3268eb133954629dbfe9b334beca30d35f9c1fca40f462ca68f9590c1bb4
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