Instructions to use ProbeX/Model-J__ResNet__model_idx_0933 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_0933 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_0933") 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_0933") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0933", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2894a42533976337f289459777b4cb5092a4f2ff131a280de1cc030057b5b1be
- Size of remote file:
- 171 MB
- SHA256:
- f625a6e56d80fe844c7b2d4256702cfe630fabd7ecc2cd0c115ba78e6e5a2f04
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