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@@ -19,8 +19,7 @@ pipeline_tag: image-classification
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  # Human-Action-Recognition-VIT-Base-patch16-224
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- This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on
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- [Bingsu/Human_Action_Recognition](https://huggingface.co/datasets/Bingsu/Human_Action_Recognition) dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: 0.4005
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  - Accuracy: 0.8786
@@ -40,7 +39,7 @@ You can use the model for image classification.
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  ### How to use
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  Here is how to use this model to classify an image of the human action into one of the following categories:
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- ['calling', 'clapping', 'cycling', 'dancing', 'drinking', 'eating', 'fighting', 'hugging', 'laughing', 'listening_to_music', 'running', 'sitting', 'sleeping', 'texting', 'using_laptop']
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  ```python
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  from transformers import pipeline
 
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  # Human-Action-Recognition-VIT-Base-patch16-224
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+ This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on [Bingsu/Human_Action_Recognition](https://huggingface.co/datasets/Bingsu/Human_Action_Recognition) dataset.
 
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  It achieves the following results on the evaluation set:
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  - Loss: 0.4005
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  - Accuracy: 0.8786
 
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  ### How to use
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  Here is how to use this model to classify an image of the human action into one of the following categories:
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+ 'calling', 'clapping', 'cycling', 'dancing', 'drinking', 'eating', 'fighting', 'hugging', 'laughing', 'listening_to_music', 'running', 'sitting', 'sleeping', 'texting', 'using_laptop'
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  ```python
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  from transformers import pipeline