Instructions to use ProbeX/Model-J__ResNet__model_idx_0349 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_0349 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_0349") 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_0349") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0349") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4d58fb080105a408be68cd0e430e9f38a293809a40c06901042945c4f09883a
- Size of remote file:
- 5.37 kB
- SHA256:
- a65c177fda0efd7980e15c7b7dd85ea422269d49e49461fda7905b41ac59f079
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