Instructions to use ProbeX/Model-J__ResNet__model_idx_0776 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_0776 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_0776") 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_0776") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0776", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a084a2a2487d5b93d79096d41272e03866ba0a397c25a7f2fae1150ec3b10b8
- Size of remote file:
- 171 MB
- SHA256:
- 9191fb026713e864ab46c358f48effaeeba0b1c4fbe95232f2bdcd718c8cf443
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