Instructions to use ProbeX/Model-J__ResNet__model_idx_0836 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_0836 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_0836") 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_0836") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0836", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5fb15ecd40182f5780a91695767060a71b858e23dba99464b4cf4c1c2596f8c5
- Size of remote file:
- 5.37 kB
- SHA256:
- 400aceabc6c2e938e7cb91b1100abed3289f103bfd217b38a66e03c9ed3ec96c
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