Instructions to use ProbeX/Model-J__ResNet__model_idx_0237 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_0237 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_0237") 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_0237") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0237", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20b2968275448047c7c7ff7fd946d3d4f7c181bba252aff510234b2297864410
- Size of remote file:
- 171 MB
- SHA256:
- d8b3408adc2007fe7da5792818c7c7911ffae1a284b2ca664e8a81771ce6f6a8
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