Instructions to use ProbeX/Model-J__ResNet__model_idx_0184 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_0184 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_0184") 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_0184") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0184", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1804f9f44711eb9453306e60d9512506b235d29cbcf88876f09b12d8a1ef5c93
- Size of remote file:
- 171 MB
- SHA256:
- 1c5b75dd5ee3db49566702e73773ebdb12e520120b56f7affd9c0bc7335c0dcf
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