Instructions to use ProbeX/Model-J__ResNet__model_idx_0136 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_0136 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_0136") 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_0136") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0136", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 346685049c184f6c97a218723ef05b2d80baf34a0ca15d57d3c8a4ff494b6866
- Size of remote file:
- 171 MB
- SHA256:
- 5eb7bd94d69d35b7f95847cb988224c7d63f6a9a76a9f16af2f182cf54d118d4
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