Instructions to use ProbeX/Model-J__ResNet__model_idx_0777 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_0777 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_0777") 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_0777") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0777", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5588907151ad92b08ebafec7a7b445c52a67dafa34b5d65e755f1f1e5190a26f
- Size of remote file:
- 5.37 kB
- SHA256:
- e458075613bbaa39e9fc3b7a4adff6937888046798cee9d6de1a33f074e4d4ab
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