Instructions to use ProbeX/Model-J__ResNet__model_idx_0769 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_0769 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_0769") 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_0769") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0769", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be4cdca4a0b93216860abe46ae1632047a016541d8a74fbcee366d8d487b9094
- Size of remote file:
- 5.37 kB
- SHA256:
- 7857ebfb585fd433a96f5e6f0ba05bbe0c05ec6f9e4435c0cc6ef68db78f5b8a
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