Instructions to use ProbeX/Model-J__ResNet__model_idx_0365 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_0365 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_0365") 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_0365") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0365", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 260cdfde5ccc9e6fa2a45c111a7d9612ae87bc34c37099f63e1d0f16cd144c3c
- Size of remote file:
- 5.37 kB
- SHA256:
- 4ec2de08f4584ddd9c370a4c4a092a1fbb9dcbb110594a0b16a511c8aa817810
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