Instructions to use ProbeX/Model-J__ResNet__model_idx_0260 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_0260 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_0260") 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_0260") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0260") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b28576c73ba09dc5082e0a60e76899962d56ed4dbc360bc92fd223dba3d2514
- Size of remote file:
- 5.37 kB
- SHA256:
- b06da6695b200656085f91b7f6ba98bef120e98ca40d6dd5228f6b7e68a32de2
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