Instructions to use ProbeX/Model-J__ResNet__model_idx_0144 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_0144 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_0144") 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_0144") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0144", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 069570d5b9b4cef2b71b4bf8b44f831d1e708849466ac8a560b74817427e3155
- Size of remote file:
- 171 MB
- SHA256:
- 02d0e82fb069c1b65df637c97d3440993d762da29436c5e684827b07aab04470
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