Instructions to use ProbeX/Model-J__ResNet__model_idx_0224 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_0224 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_0224", device_map="auto") 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_0224") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 33f1ba3fd5794b08ff22f86990cbe1018843c45a710ed2645f921c0cd6cd321d
- Size of remote file:
- 5.37 kB
- SHA256:
- 5ca817aa37d1e12a2261340accf06155eb840f8b6e1aaa33c4b2c30b8f8c9870
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