Instructions to use ProbeX/Model-J__ResNet__model_idx_0270 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_0270 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_0270") 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_0270") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0270", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12e44ee9368d95b7da951141fca0f5c9e755d39df1b3a907099b38e3db9161f7
- Size of remote file:
- 5.37 kB
- SHA256:
- 3b9093835b92cad73e15830e5249ba1b5a4138c43239321940faa906a299751a
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