Instructions to use ProbeX/Model-J__ResNet__model_idx_0087 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_0087 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_0087") 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_0087") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0087", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4394458e844427a7d6c44271fe8b2d6271bc30d90ea70cc8ec92d2bb7e420637
- Size of remote file:
- 171 MB
- SHA256:
- 2d4c7ad54992d357023144aa177c6660e5b232db9327433d9912b41b6936c37d
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