Instructions to use ProbeX/Model-J__ResNet__model_idx_0334 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_0334 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_0334") 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_0334") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0334", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee5aa9e040606808b6eefcc6fe05b4f860e3b63a8c736452e6961558de54e667
- Size of remote file:
- 171 MB
- SHA256:
- 5f4ee18098017f57ebcb4e8f21abf4eaa5c43a9ab1644cf71ee8475880e133f2
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