Instructions to use ProbeX/Model-J__ResNet__model_idx_0997 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_0997 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_0997") 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_0997") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0997", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff243b7960cb468af5c4994be037db974e531beabf880f87c3109d8616304188
- Size of remote file:
- 5.37 kB
- SHA256:
- c0ce5689a3558a58245af7d44b9f1c30ac867cba77a95f1bd472c5839f6438c5
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