Instructions to use ProbeX/Model-J__ResNet__model_idx_0984 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_0984 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_0984") 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_0984") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0984", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44e304869f8e5caeda6f36f20ce52907ddb5d65525a14b26e8bec95b9217f1a2
- Size of remote file:
- 171 MB
- SHA256:
- 3426ffacc9b1c8d1800fc1a65aeffd932b51dd77390bf45be8dbdf4a66f078fd
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