Instructions to use ProbeX/Model-J__ResNet__model_idx_0779 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_0779 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_0779") 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_0779") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0779", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20da8735fe687cdeb6a4f382b1f2c3bc742904994ac3f7e28f5fb931893a9d26
- Size of remote file:
- 5.37 kB
- SHA256:
- 94cd0ec3ad76aaaae2562626c4b6b1d035887dfd21e8bd8b2bed0872e213e1d5
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