Instructions to use ProbeX/Model-J__ResNet__model_idx_0201 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_0201 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_0201") 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_0201") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0201", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65656e4b028050b0a03008add2a403e7216d6cad62eae47eb99eab9183aef663
- Size of remote file:
- 5.37 kB
- SHA256:
- a83525aa528243881cd4394dc5806043f066d58cfa39e3f9ebe0fac87edd33f0
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