Instructions to use ProbeX/Model-J__ResNet__model_idx_0888 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_0888 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_0888") 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_0888") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0888", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bdd4b68cd663221ada44c7e4f6bd8788b66c2c11d3ccf6f021da2cfd365a0fce
- Size of remote file:
- 5.37 kB
- SHA256:
- 135fd5614ce94d98594326222b4e5034924f31865c1ef804bf48a66adc6e48eb
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