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")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Thamer/resnet-fine_tuned") model = AutoModelForImageClassification.from_pretrained("Thamer/resnet-fine_tuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (3a4e824eb05229fd99d456f7a2ca7507e1bee20c) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>0e2d501
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