Instructions to use ProbeX/Model-J__ResNet__model_idx_0352 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_0352 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_0352") 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_0352") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0352", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bd96dce75dd50d84c65853744b1590a028b3ccc941436d07aed8e0ef6af6992
- Size of remote file:
- 171 MB
- SHA256:
- 09a4b2f38e8d636c93038cb3cb68b611bfc685374b3503f8166a3cc92f124aee
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