Instructions to use ProbeX/Model-J__ResNet__model_idx_0435 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_0435 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_0435") 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_0435") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0435", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 403402cc4632761a19140e36b3a7d0c04ffe8b1d3b1a24ffb3adcf87a1a94c00
- Size of remote file:
- 171 MB
- SHA256:
- 8d0b67318eb9f3439ba16f3758da4c9c42264e17d06f22f0f489986a6b52a861
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