Instructions to use ProbeX/Model-J__ResNet__model_idx_0076 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_0076 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_0076") 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_0076") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0076", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5ce5ed642a4dd33c66bd643e0b898105da8477df524f94ebabb13734665fba8
- Size of remote file:
- 5.37 kB
- SHA256:
- b8d72851014a3af1a4355b353b8ec235be667190340476f7d854c5f6c2a41222
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