Instructions to use ProbeX/Model-J__ResNet__model_idx_0144 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_0144 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_0144") 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_0144") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0144") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 62a917622ef60e9ac9f382e18954f567864defbcf9a2eb516f4a0947af6fae18
- Size of remote file:
- 5.37 kB
- SHA256:
- 402bf27f13f3888c062544d43d469203dff7cf15984182ce337a551b3e0070d6
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