Instructions to use ProbeX/Model-J__ResNet__model_idx_0195 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_0195 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_0195") 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_0195") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0195") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 10504a8115c299eecbfc340d30a71a0cbb525f7f47f91bc10367a277e9ad4d04
- Size of remote file:
- 5.37 kB
- SHA256:
- 7b4a620313cae234dfd08bd42873551f2615f57c6a163397b74667a366d65c4a
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