Instructions to use ProbeX/Model-J__ResNet__model_idx_0885 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_0885 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_0885") 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_0885") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0885", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe84c790e52164baea023393df651690c04e026846a37db971b31b0999a0afdf
- Size of remote file:
- 5.37 kB
- SHA256:
- 9b830fa104195da9bb412eb23079438b34eab856bf9cb262fa66777af776c275
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