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--- |
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license: apache-2.0 |
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base_model: microsoft/swinv2-tiny-patch4-window16-256 |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: swinv2-tiny-patch4-window16-256-finetuned-tekno24 |
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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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# swinv2-tiny-patch4-window16-256-finetuned-tekno24 |
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This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window16-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window16-256) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2871 |
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- Accuracy: 0.4224 |
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- F1: 0.3135 |
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- Precision: 0.4313 |
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- Recall: 0.4224 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 1.3705 | 0.9963 | 68 | 1.3584 | 0.3563 | 0.2590 | 0.2886 | 0.3563 | |
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| 1.3515 | 1.9927 | 136 | 1.3392 | 0.3921 | 0.2606 | 0.3925 | 0.3921 | |
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| 1.3498 | 2.9890 | 204 | 1.3301 | 0.3912 | 0.2501 | 0.4247 | 0.3912 | |
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| 1.3351 | 4.0 | 273 | 1.3225 | 0.3930 | 0.2452 | 0.5371 | 0.3930 | |
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| 1.3212 | 4.9963 | 341 | 1.3128 | 0.3949 | 0.2556 | 0.4641 | 0.3949 | |
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| 1.3316 | 5.9927 | 409 | 1.3052 | 0.4004 | 0.2723 | 0.4129 | 0.4004 | |
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| 1.3269 | 6.9890 | 477 | 1.2980 | 0.4068 | 0.2850 | 0.4305 | 0.4068 | |
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| 1.3034 | 8.0 | 546 | 1.2927 | 0.4123 | 0.2924 | 0.4448 | 0.4123 | |
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| 1.3165 | 8.9963 | 614 | 1.2884 | 0.4215 | 0.3096 | 0.4453 | 0.4215 | |
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| 1.3306 | 9.9634 | 680 | 1.2871 | 0.4224 | 0.3135 | 0.4313 | 0.4224 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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