Instructions to use ProbeX/Model-J__ResNet__model_idx_0962 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_0962 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_0962") 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_0962") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0962", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 773728e1f21cd1ea3cf741d48d64fd2a02328da487f86d8dd23e579d90bbef19
- Size of remote file:
- 5.37 kB
- SHA256:
- b3cb4560b6ba5f4ee8f86b99420a7c9a1495273df465f3b822f3e60363892ba1
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