Instructions to use ProbeX/Model-J__ResNet__model_idx_0716 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_0716 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_0716") 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_0716") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0716", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b7f2648292ed762e39139d80ff08c01174dba3f365fb447c394d89ce8da8584
- Size of remote file:
- 171 MB
- SHA256:
- 87cc5ae6a118c559a850a6717f01126533eec0c6a427d90b21903d2eda7ae129
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