Instructions to use ProbeX/Model-J__ResNet__model_idx_0213 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_0213 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_0213") 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_0213") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0213", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 761009074330403131bea762165cef43b6952b3b0ac3be8bdba00f548432dce7
- Size of remote file:
- 5.37 kB
- SHA256:
- ec9def3db4ba6b65dbcdbe90e78b6471177715549259f8e6decb96d85c0cacc1
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