Instructions to use ProbeX/Model-J__ResNet__model_idx_0489 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_0489 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_0489") 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_0489") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0489", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 169abeb7f3fa512c5f63229b705114692c227c659037537587b11081007835b0
- Size of remote file:
- 5.37 kB
- SHA256:
- b7b78e3f4f03fbd157f26786c98ec0cebda678a9f72532046c8cd6b65702b980
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