Instructions to use ProbeX/Model-J__ResNet__model_idx_0468 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_0468 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_0468") 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_0468") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0468") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59ed25796727f29d86993105517cc7c96deff44fe986f0b8eab20afd06201b05
- Size of remote file:
- 5.37 kB
- SHA256:
- 0378dc4edf5568f8237c8f1ea8943e531ee8530dd918f09468beabdc4b188b57
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