Instructions to use ProbeX/Model-J__ResNet__model_idx_0347 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_0347 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_0347") 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_0347") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0347") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1756c4fced0fd904b5749aa64d02988c12ebe638d4c9f3477997c9e8efb826ae
- Size of remote file:
- 5.37 kB
- SHA256:
- aac1aaa19780314e2bda7e1e2df61ed1ef242404b7bd8208e14d78e2f0a19003
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