Instructions to use ProbeX/Model-J__ResNet__model_idx_0447 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_0447 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_0447") 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_0447") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0447") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de7e26e76bbc802dd45a66611c2c2d9fa9942f54b5d9667b59619eb01bfc5171
- Size of remote file:
- 5.37 kB
- SHA256:
- 1b4900db9c1266371c1c7bce18fd47f3eb468f59a6af3d34d0733dd905967cf6
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