Instructions to use ProbeX/Model-J__ResNet__model_idx_0016 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_0016 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_0016") 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_0016") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0016", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf2c7c83259f829001d71714ef60b2f0bd191882f1c30e5afa68f276238e55ce
- Size of remote file:
- 171 MB
- SHA256:
- 7c9dfda7b089a98867cc97aee483cb7adf6fe9b466f947439cdb4ddec8e72fff
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