Instructions to use ProbeX/Model-J__ResNet__model_idx_0384 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_0384 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_0384") 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_0384") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf937e194c84f6249c3f1f06f2cf62ecd752218be599163ddb96aebe0002806c
- Size of remote file:
- 5.37 kB
- SHA256:
- e14e277de34ca198a8b92ba9a7e4574e96b4266120446769322e1835d80cd899
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