Instructions to use ProbeX/Model-J__ResNet__model_idx_0580 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_0580 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_0580") 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_0580") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0580") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9268c5962a624e7acf07457cb0e3b9a2c30b0959118e311f997708696bd7b42
- Size of remote file:
- 5.37 kB
- SHA256:
- e9d6c2687bf35c57cb34ced20c5f52621064bcb919c4cb97e13dfdf6cb15c66c
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