Instructions to use ProbeX/Model-J__ResNet__model_idx_0699 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_0699 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_0699") 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_0699") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0699", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 33896214c35d07cfee93e6479a6254b2fb262bd4a3db15a5b633ee33e7dc304e
- Size of remote file:
- 5.37 kB
- SHA256:
- 5404b39f326f974cfd16aa04a408cc9965548ef6487d7f9a290b7f4eb7a8141f
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