Instructions to use ProbeX/Model-J__ResNet__model_idx_0177 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_0177 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_0177") 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_0177") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0177") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c576dc9dc6dbe8e1b3007faf9f010f48a570a86f88000535562918cbb15bd09
- Size of remote file:
- 5.37 kB
- SHA256:
- 9ebf666548ab9f931629d5ad4063b134acb65108126ff30b2cdade33e8fe7ebc
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