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--- |
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license: other |
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base_model: neuralhaven/KDRSSC_TinyViT2MobileViT-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_TinyViT2MobileViT-xx-small-RESISC45_01 |
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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_TinyViT2MobileViT-xx-small-RESISC45_01 |
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This model is a fine-tuned version of [neuralhaven/KDRSSC_TinyViT2MobileViT-xx-small](https://huggingface.co./neuralhaven/KDRSSC_TinyViT2MobileViT-xx-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5369 |
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- Accuracy: 0.8510 |
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- Precision: 0.8555 |
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- Recall: 0.8522 |
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- F1: 0.8521 |
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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: 0.0005 |
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- train_batch_size: 256 |
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- eval_batch_size: 256 |
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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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| 1.1561 | 1.0 | 20 | 0.8622 | 0.7856 | 0.8025 | 0.7860 | 0.7842 | |
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| 0.9463 | 2.0 | 40 | 0.7009 | 0.8127 | 0.8247 | 0.8136 | 0.8142 | |
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| 0.7867 | 3.0 | 60 | 0.7666 | 0.7817 | 0.8076 | 0.7824 | 0.7819 | |
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| 0.7504 | 4.0 | 80 | 0.6580 | 0.8203 | 0.8240 | 0.8214 | 0.8194 | |
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| 0.7147 | 5.0 | 100 | 0.6410 | 0.8208 | 0.8348 | 0.8204 | 0.8221 | |
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| 0.6715 | 6.0 | 120 | 0.5923 | 0.8343 | 0.8390 | 0.8360 | 0.8356 | |
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| 0.6114 | 7.0 | 140 | 0.5965 | 0.8321 | 0.8415 | 0.8338 | 0.8330 | |
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| 0.6041 | 8.0 | 160 | 0.5625 | 0.8440 | 0.8522 | 0.8448 | 0.8456 | |
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| 0.5449 | 9.0 | 180 | 0.5466 | 0.8457 | 0.8508 | 0.8470 | 0.8472 | |
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| 0.5607 | 10.0 | 200 | 0.5369 | 0.8510 | 0.8555 | 0.8522 | 0.8521 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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