Instructions to use ProbeX/Model-J__ResNet__model_idx_0345 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_0345 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_0345") 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_0345") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0345", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed3f0858115b07a263a0f8d74e477a8d164930983d05351488137064176c9dd5
- Size of remote file:
- 5.37 kB
- SHA256:
- 0579cb32414b08d58dc558c3cb6d67dd5fd3cf844b335323df6b62c380b1ecb4
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