Instructions to use ProbeX/Model-J__ResNet__model_idx_0830 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_0830 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_0830") 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_0830") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0830", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f793723dc2bc8ac0f244f3c112feb74c323fc0897e991720174ce4c8c52c223d
- Size of remote file:
- 171 MB
- SHA256:
- 3c48ae32dddb1d958eab86716cc162af6e88382da4d77e0bb417b5a788a91607
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