Instructions to use ProbeX/Model-J__ResNet__model_idx_0508 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_0508 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_0508") 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_0508") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0508", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d68a4a1ad073de026de493f9ba143b3204c86207e38f48c73c992980efd6f00d
- Size of remote file:
- 5.37 kB
- SHA256:
- 6c3bf4427ed1f1a0ef1dc0b1d4c7fd66b2cd17913cfa36ad68dc29121a6456be
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