Instructions to use ProbeX/Model-J__ResNet__model_idx_0955 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_0955 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_0955") 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_0955") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0955", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e362e1da3dbd0e81260c52c6bf58ceed09f78a1349e841645a88594ca310b144
- Size of remote file:
- 5.37 kB
- SHA256:
- d318f865cc06fae7519d838a6933c06b39692ce4bd4d412f7d236b76f6cd9892
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