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--- |
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library_name: transformers |
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license: other |
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base_model: apple/mobilevit-xx-small |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: KDRSSC_ViT2MobileViT-xx-small |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# KDRSSC_ViT2MobileViT-xx-small |
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This model is a fine-tuned version of [apple/mobilevit-xx-small](https://huggingface.co/apple/mobilevit-xx-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6274 |
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- Accuracy: 0.8495 |
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- Precision: 0.8504 |
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- Recall: 0.8501 |
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- F1: 0.8440 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.6963 | 1.0 | 148 | 1.3476 | 0.596 | 0.6092 | 0.5736 | 0.5557 | |
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| 1.2335 | 2.0 | 296 | 1.0216 | 0.725 | 0.7180 | 0.7135 | 0.6918 | |
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| 0.9693 | 3.0 | 444 | 0.8330 | 0.776 | 0.7560 | 0.7699 | 0.7481 | |
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| 0.8246 | 4.0 | 592 | 0.7345 | 0.812 | 0.8091 | 0.8042 | 0.7889 | |
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| 0.7393 | 5.0 | 740 | 0.6836 | 0.828 | 0.8084 | 0.8223 | 0.8070 | |
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| 0.6895 | 6.0 | 888 | 0.6504 | 0.831 | 0.8245 | 0.8253 | 0.8134 | |
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| 0.6528 | 7.0 | 1036 | 0.6252 | 0.859 | 0.8546 | 0.8571 | 0.8461 | |
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| 0.6303 | 8.0 | 1184 | 0.6089 | 0.856 | 0.8506 | 0.8554 | 0.8444 | |
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| 0.6138 | 9.0 | 1332 | 0.6002 | 0.863 | 0.8567 | 0.8632 | 0.8519 | |
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| 0.6067 | 10.0 | 1480 | 0.6003 | 0.863 | 0.8596 | 0.8624 | 0.8521 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.0 |
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- Tokenizers 0.19.1 |
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