Instructions to use ProbeX/Model-J__ResNet__model_idx_0174 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_0174 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_0174") 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_0174") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0174", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c14d8f0f3b9a21bbe57369dbfc6ebe9fd65967c4fc445be05f4607f2719989eb
- Size of remote file:
- 171 MB
- SHA256:
- 05b85c49779ba55b417c5247ce626d0adb5b96cecbd0a3ba8deea4b2abb12f3e
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