Instructions to use ProbeX/Model-J__ResNet__model_idx_0689 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_0689 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_0689") 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_0689") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0689") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7fd3c388122a2c889aa779c19b6c29f8e6e0f2492b6c56557772018078646176
- Size of remote file:
- 5.37 kB
- SHA256:
- 17fd6efe0a76e19f40fd068a2686f5847f225e783f1d4e38b385ae7a9b48366c
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