Instructions to use ProbeX/Model-J__ResNet__model_idx_0342 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_0342 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_0342") 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_0342") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0342") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ede69f9adf621fece0313e4ea2e29281c63c751cf3726cd5db35a8f3d89621c
- Size of remote file:
- 5.37 kB
- SHA256:
- 74b16236f62a76172f74aa1098e7293c9c5d9e443aa4a44363bf3359f14775ce
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