Instructions to use ProbeX/Model-J__ResNet__model_idx_0820 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_0820 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_0820") 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_0820") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0820") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 67f2d49788b0638c02939b597750c6dbd91880831162455af6dde30d6d3efa5a
- Size of remote file:
- 5.37 kB
- SHA256:
- eba36e4e2c2d3a31c69b7952c839a4b91cc2b71178de4edc885c1ec955ea317e
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