Instructions to use ProbeX/Model-J__ResNet__model_idx_0229 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_0229 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_0229") 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_0229") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0229", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e0ec1cb8c69d86ee258c63cc7f60955cd3dd58c545aa0431d376deef591d63a
- Size of remote file:
- 5.37 kB
- SHA256:
- 2993b6b783154a8749c7f9a053c5a6e8c77b987400251fae54a19d17f9e67ab6
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