Instructions to use ProbeX/Model-J__ResNet__model_idx_0635 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_0635 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_0635") 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_0635") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0635", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9cc7c143fab90909f5dce6326a996dd3ed79c30e2228240d6c7a6860fc0efe72
- Size of remote file:
- 171 MB
- SHA256:
- 2ac512cb703623e18376669ed81e409a8aaf908461b77fb8b782d7b5b99fd2d5
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