Instructions to use vuongnhathien/save-model-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vuongnhathien/save-model-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="vuongnhathien/save-model-final") 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("vuongnhathien/save-model-final") model = AutoModelForImageClassification.from_pretrained("vuongnhathien/save-model-final") - Notebooks
- Google Colab
- Kaggle
5 epoch 2nd
Browse files
README.md
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license: apache-2.0
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base_model: microsoft/swinv2-tiny-patch4-window16-256
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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# save-model-final
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This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window16-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window16-256) on
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It achieves the following results on the evaluation set:
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- Loss: 0.3658
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- Accuracy: 0.875
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license: apache-2.0
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base_model: microsoft/swinv2-tiny-patch4-window16-256
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tags:
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- image-classification
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- generated_from_trainer
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metrics:
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- accuracy
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# save-model-final
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This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window16-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window16-256) on the jbarat/plant_species dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3658
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- Accuracy: 0.875
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