Instructions to use ProbeX/Model-J__ResNet__model_idx_0725 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_0725 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_0725") 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_0725") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0725", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 52a6a7d5f162d87769dd4ad126df9521dd127a6aa78aeb55e31a43a899af7cd8
- Size of remote file:
- 5.37 kB
- SHA256:
- f21bf642b46df4766047da81b228a484afde5971cc442a450ea05ce78f57d85e
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