Instructions to use ProbeX/Model-J__ResNet__model_idx_0729 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_0729 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_0729") 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_0729") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0729", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f729918fe8e0cdc5c7213fe72c7ec40794d30b9f1ddb876a3ddb63f1e97d054
- Size of remote file:
- 171 MB
- SHA256:
- 157aaa3a5708cb14dc4459bef6f16d0c59d48352eeb8995fe8293f2bd44b648a
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