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--- |
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license: apache-2.0 |
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base_model: neuralhaven/KDRSSC_ViT2TinyViT |
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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_ViT2TinyViT-RESISC45_FT |
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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_ViT2TinyViT-RESISC45_FT |
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This model is a fine-tuned version of [neuralhaven/KDRSSC_ViT2TinyViT](https://huggingface.co/neuralhaven/KDRSSC_ViT2TinyViT) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2192 |
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- Accuracy: 0.9403 |
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- Precision: 0.9412 |
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- Recall: 0.9410 |
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- F1: 0.9406 |
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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: 512 |
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- eval_batch_size: 512 |
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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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| 3.1125 | 1.0 | 37 | 0.9645 | 0.911 | 0.9167 | 0.9076 | 0.9069 | |
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| 0.6036 | 2.0 | 74 | 0.2854 | 0.938 | 0.9387 | 0.9394 | 0.9370 | |
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| 0.4344 | 3.0 | 111 | 0.2315 | 0.942 | 0.9412 | 0.9422 | 0.9395 | |
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| 0.3572 | 4.0 | 148 | 0.1993 | 0.948 | 0.9480 | 0.9487 | 0.9464 | |
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| 0.3086 | 5.0 | 185 | 0.2025 | 0.94 | 0.9405 | 0.9391 | 0.9372 | |
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| 0.2906 | 6.0 | 222 | 0.1979 | 0.939 | 0.9394 | 0.9381 | 0.9358 | |
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| 0.2567 | 7.0 | 259 | 0.1814 | 0.943 | 0.9427 | 0.9440 | 0.9413 | |
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| 0.2785 | 8.0 | 296 | 0.1563 | 0.948 | 0.9470 | 0.9484 | 0.9464 | |
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| 0.2462 | 9.0 | 333 | 0.1509 | 0.951 | 0.9508 | 0.9524 | 0.9501 | |
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| 0.245 | 10.0 | 370 | 0.1489 | 0.949 | 0.9475 | 0.9492 | 0.9468 | |
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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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