Instructions to use yfh/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yfh/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="yfh/image_classification") 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("yfh/image_classification") model = AutoModelForImageClassification.from_pretrained("yfh/image_classification") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 2.
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- eval_accuracy: 0.
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- eval_runtime: 2.
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- eval_samples_per_second:
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- eval_steps_per_second: 4.
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- epoch: 38.
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- step:
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## Model description
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 2.7619
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- eval_accuracy: 0.475
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- eval_runtime: 2.0446
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- eval_samples_per_second: 78.255
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- eval_steps_per_second: 4.891
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- epoch: 38.73
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- step: 1549
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## Model description
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