Instructions to use Thamer/resnet-fine_tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thamer/resnet-fine_tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Thamer/resnet-fine_tuned") 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("Thamer/resnet-fine_tuned") model = AutoModelForImageClassification.from_pretrained("Thamer/resnet-fine_tuned") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da24d360a6ed40e0b97241447175eda1d2528fee86ec04657eea79a830a1199a
- Size of remote file:
- 85.3 MB
- SHA256:
- 14b0ca479b957d1604e175ae06952b08e8a984d55c98c07729b8a28d6c2b144f
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