Instructions to use ProbeX/Model-J__ResNet__model_idx_0420 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_0420 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_0420") 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_0420") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0420") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8984f70f6e19b8e093e0c3f31b2a75f00b32152c4b4de5b94e0b865c6f43f42
- Size of remote file:
- 5.37 kB
- SHA256:
- ae32fb1bc9e468511ab3abefcea6df1dc1920f9274cc8094317f6ea75ebe3256
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