Instructions to use ProbeX/Model-J__ResNet__model_idx_0679 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_0679 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_0679") 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_0679") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0679", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 186d9afdb0ffaf3e15f94391b65e9bd4e8b03b189f0b89e22ae2613cc7a681ad
- Size of remote file:
- 171 MB
- SHA256:
- e8946a351747b1606ee9e041bf30a303bbece9c9c5b146615853549f2228f6e9
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