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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: microsoft/swinv2-tiny-patch4-window8-256 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: swinv2-tiny-patch4-window8-256-RD-FIX |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.782608695652174 |
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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-window8-256-RD-FIX |
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This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5014 |
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- Accuracy: 0.7826 |
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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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 40 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:| |
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| No log | 0.8571 | 3 | 1.1955 | 0.4565 | |
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| No log | 1.8571 | 6 | 1.1280 | 0.5 | |
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| No log | 2.8571 | 9 | 1.0565 | 0.4783 | |
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| 4.8751 | 3.8571 | 12 | 0.9184 | 0.5870 | |
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| 4.8751 | 4.8571 | 15 | 0.8208 | 0.5870 | |
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| 4.8751 | 5.8571 | 18 | 0.7310 | 0.6087 | |
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| 3.6315 | 6.8571 | 21 | 0.6951 | 0.7174 | |
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| 3.6315 | 7.8571 | 24 | 0.6772 | 0.7174 | |
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| 3.6315 | 8.8571 | 27 | 0.6626 | 0.7174 | |
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| 2.8559 | 9.8571 | 30 | 0.5987 | 0.7826 | |
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| 2.8559 | 10.8571 | 33 | 0.5431 | 0.8261 | |
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| 2.8559 | 11.8571 | 36 | 0.6193 | 0.6739 | |
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| 2.8559 | 12.8571 | 39 | 0.6475 | 0.7174 | |
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| 2.3617 | 13.8571 | 42 | 0.5725 | 0.7174 | |
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| 2.3617 | 14.8571 | 45 | 0.5794 | 0.7826 | |
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| 2.3617 | 15.8571 | 48 | 0.5292 | 0.7826 | |
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| 2.1506 | 16.8571 | 51 | 0.5988 | 0.7391 | |
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| 2.1506 | 17.8571 | 54 | 0.6548 | 0.7174 | |
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| 2.1506 | 18.8571 | 57 | 0.5131 | 0.8261 | |
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| 1.9498 | 19.8571 | 60 | 0.4700 | 0.8478 | |
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| 1.9498 | 20.8571 | 63 | 0.5254 | 0.8043 | |
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| 1.9498 | 21.8571 | 66 | 0.5451 | 0.7826 | |
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| 1.9498 | 22.8571 | 69 | 0.5304 | 0.7609 | |
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| 1.422 | 23.8571 | 72 | 0.5105 | 0.8043 | |
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| 1.422 | 24.8571 | 75 | 0.4685 | 0.7826 | |
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| 1.422 | 25.8571 | 78 | 0.4875 | 0.8261 | |
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| 1.3044 | 26.8571 | 81 | 0.5492 | 0.7826 | |
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| 1.3044 | 27.8571 | 84 | 0.5202 | 0.7826 | |
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| 1.3044 | 28.8571 | 87 | 0.4737 | 0.8261 | |
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| 1.2464 | 29.8571 | 90 | 0.4398 | 0.8478 | |
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| 1.2464 | 30.8571 | 93 | 0.4753 | 0.8043 | |
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| 1.2464 | 31.8571 | 96 | 0.4913 | 0.8043 | |
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| 1.2464 | 32.8571 | 99 | 0.5262 | 0.7826 | |
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| 1.1614 | 33.8571 | 102 | 0.5280 | 0.7826 | |
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| 1.1614 | 34.8571 | 105 | 0.5252 | 0.7609 | |
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| 1.1614 | 35.8571 | 108 | 0.5127 | 0.7826 | |
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| 1.045 | 36.8571 | 111 | 0.5061 | 0.7826 | |
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| 1.045 | 37.8571 | 114 | 0.5012 | 0.7826 | |
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| 1.045 | 38.8571 | 117 | 0.5025 | 0.7826 | |
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| 0.9391 | 39.8571 | 120 | 0.5014 | 0.7826 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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