Instructions to use ProbeX/Model-J__ResNet__model_idx_0152 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_0152 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_0152") 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_0152") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0152") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 367ed9160c8addad65dcb0e234dbf3433ed8c6e0d67072936d5c688485679161
- Size of remote file:
- 5.37 kB
- SHA256:
- d1cd88ff13741491e587ddf347d5e81f71408bdfd48479b9916ee49cb01af57f
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