Instructions to use ProbeX/Model-J__ResNet__model_idx_0209 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_0209 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_0209") 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_0209") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0209") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d1b37c3d737e23495c0001a541eefdfd7eb2e90a1fe49ad561848598c8fc5114
- Size of remote file:
- 5.37 kB
- SHA256:
- 8418b790e1780e7dbc797270b350153b942aae8f7acdc7d58e1eea12d7e37076
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