Instructions to use ProbeX/Model-J__ResNet__model_idx_0901 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_0901 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_0901") 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_0901") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0901", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae90087caa29259fa19e5bddd2e4732ef4c7ea1c568a8cafff97d9c412a04d75
- Size of remote file:
- 171 MB
- SHA256:
- b8b06250970e30cfc3c6e84797ca497a3e211957cbae05242fb522bbb2ac0b84
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