Instructions to use ProbeX/Model-J__ResNet__model_idx_0764 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_0764 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_0764") 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_0764") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0764", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29f7490daf81193e6db8c77a89f6a6538a6cb8beb69169acc84e8c9f5c97b06d
- Size of remote file:
- 171 MB
- SHA256:
- 1659d11b0f3e61b3bf6d996613b098f330bf3afa52d1a3e8f5f9568d25503fb6
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