Instructions to use ProbeX/Model-J__ResNet__model_idx_0298 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_0298 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_0298") 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_0298") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0298") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12acb832a78dfc91e9159153ee0d507886f9f04ee9be20047fd6361e3d930ff0
- Size of remote file:
- 5.37 kB
- SHA256:
- 934153b957bc1000bc819e936af3bf666cfbbccaccb82fd9436d8d6da8c20d19
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