Instructions to use ProbeX/Model-J__ResNet__model_idx_0104 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_0104 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_0104") 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_0104") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0104") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15fcf3a1983346fdf8ff2dda7477be5aee5dba6d09d1da436152b5107deae75e
- Size of remote file:
- 5.37 kB
- SHA256:
- bda93f45de6e27409ab186d6ad7df6343de6700c8c1c1a0a06ddf9658f2904e2
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