Instructions to use ProbeX/Model-J__ResNet__model_idx_0906 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_0906 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_0906") 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_0906") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0906", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 010d4ce4fceb0fa986b06ac6eaebee9e7f1064ae202f2df785c489778364d234
- Size of remote file:
- 171 MB
- SHA256:
- 40a99a59a32a3350ef24845fb4f89a4c0cc0c1aab01fdc574a4c547a51e8e4c2
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