Instructions to use ProbeX/Model-J__ResNet__model_idx_0449 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_0449 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_0449") 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_0449") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0449", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d93ed35b06b5515bcbb9fba2d989a648c6c50638c21a3fdd7ce3acc0ef838bce
- Size of remote file:
- 5.37 kB
- SHA256:
- dbe55ca880467cf79e2cf868ed417b9f06daa4bcfee75a66be84439157fc9b22
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