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--- |
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license: apache-2.0 |
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base_model: microsoft/beit-base-patch16-224 |
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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: beit-base-patch16-224-fold1 |
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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: train |
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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: 0.8481012658227848 |
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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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# beit-base-patch16-224-fold1 |
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This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7241 |
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- Accuracy: 0.8481 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 100 |
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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 | 0.8050 | 0.4557 | |
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| No log | 2.0 | 7 | 0.7151 | 0.5696 | |
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| 0.8103 | 2.8571 | 10 | 0.6822 | 0.5570 | |
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| 0.8103 | 4.0 | 14 | 0.6408 | 0.5696 | |
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| 0.8103 | 4.8571 | 17 | 0.6244 | 0.6709 | |
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| 0.6583 | 6.0 | 21 | 0.5893 | 0.6709 | |
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| 0.6583 | 6.8571 | 24 | 0.5877 | 0.6329 | |
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| 0.6583 | 8.0 | 28 | 0.5752 | 0.6835 | |
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| 0.5912 | 8.8571 | 31 | 0.5826 | 0.6456 | |
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| 0.5912 | 10.0 | 35 | 0.5469 | 0.6835 | |
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| 0.5912 | 10.8571 | 38 | 0.6173 | 0.6582 | |
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| 0.5301 | 12.0 | 42 | 0.5151 | 0.6962 | |
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| 0.5301 | 12.8571 | 45 | 0.5105 | 0.6962 | |
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| 0.5301 | 14.0 | 49 | 0.5489 | 0.7089 | |
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| 0.4703 | 14.8571 | 52 | 0.5725 | 0.6835 | |
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| 0.4703 | 16.0 | 56 | 0.5560 | 0.6962 | |
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| 0.4703 | 16.8571 | 59 | 0.5824 | 0.6709 | |
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| 0.4189 | 18.0 | 63 | 0.5401 | 0.7468 | |
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| 0.4189 | 18.8571 | 66 | 0.5147 | 0.7722 | |
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| 0.3741 | 20.0 | 70 | 0.4864 | 0.7595 | |
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| 0.3741 | 20.8571 | 73 | 0.5272 | 0.7342 | |
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| 0.3741 | 22.0 | 77 | 0.4914 | 0.7468 | |
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| 0.387 | 22.8571 | 80 | 0.5658 | 0.7468 | |
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| 0.387 | 24.0 | 84 | 0.4662 | 0.7722 | |
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| 0.387 | 24.8571 | 87 | 0.4376 | 0.7848 | |
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| 0.3502 | 26.0 | 91 | 0.5367 | 0.7722 | |
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| 0.3502 | 26.8571 | 94 | 0.5490 | 0.7342 | |
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| 0.3502 | 28.0 | 98 | 0.7163 | 0.7722 | |
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| 0.3148 | 28.8571 | 101 | 0.6005 | 0.7468 | |
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| 0.3148 | 30.0 | 105 | 0.6501 | 0.7722 | |
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| 0.3148 | 30.8571 | 108 | 0.5313 | 0.7975 | |
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| 0.2973 | 32.0 | 112 | 0.5466 | 0.7722 | |
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| 0.2973 | 32.8571 | 115 | 0.5731 | 0.8101 | |
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| 0.2973 | 34.0 | 119 | 0.6544 | 0.8101 | |
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| 0.2474 | 34.8571 | 122 | 0.6061 | 0.7848 | |
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| 0.2474 | 36.0 | 126 | 0.5816 | 0.7722 | |
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| 0.2474 | 36.8571 | 129 | 0.7161 | 0.7595 | |
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| 0.2033 | 38.0 | 133 | 0.6235 | 0.7848 | |
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| 0.2033 | 38.8571 | 136 | 0.7889 | 0.7595 | |
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| 0.2338 | 40.0 | 140 | 0.5943 | 0.7595 | |
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| 0.2338 | 40.8571 | 143 | 0.6170 | 0.7342 | |
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| 0.2338 | 42.0 | 147 | 0.6964 | 0.6962 | |
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| 0.2067 | 42.8571 | 150 | 0.7154 | 0.7468 | |
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| 0.2067 | 44.0 | 154 | 0.7675 | 0.7722 | |
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| 0.2067 | 44.8571 | 157 | 0.7766 | 0.7468 | |
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| 0.2133 | 46.0 | 161 | 0.9330 | 0.7848 | |
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| 0.2133 | 46.8571 | 164 | 0.6494 | 0.7975 | |
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| 0.2133 | 48.0 | 168 | 0.5709 | 0.7722 | |
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| 0.2004 | 48.8571 | 171 | 0.6462 | 0.8101 | |
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| 0.2004 | 50.0 | 175 | 0.6668 | 0.7722 | |
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| 0.2004 | 50.8571 | 178 | 0.6305 | 0.8101 | |
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| 0.188 | 52.0 | 182 | 0.7189 | 0.8228 | |
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| 0.188 | 52.8571 | 185 | 0.6853 | 0.7848 | |
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| 0.188 | 54.0 | 189 | 0.8040 | 0.8228 | |
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| 0.1623 | 54.8571 | 192 | 0.6958 | 0.8101 | |
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| 0.1623 | 56.0 | 196 | 0.6907 | 0.8101 | |
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| 0.1623 | 56.8571 | 199 | 0.6821 | 0.8101 | |
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| 0.1588 | 58.0 | 203 | 0.6534 | 0.8101 | |
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| 0.1588 | 58.8571 | 206 | 0.7192 | 0.8101 | |
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| 0.1607 | 60.0 | 210 | 0.7753 | 0.8228 | |
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| 0.1607 | 60.8571 | 213 | 0.8950 | 0.8101 | |
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| 0.1607 | 62.0 | 217 | 0.7904 | 0.8101 | |
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| 0.1767 | 62.8571 | 220 | 0.6973 | 0.8101 | |
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| 0.1767 | 64.0 | 224 | 0.6694 | 0.7975 | |
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| 0.1767 | 64.8571 | 227 | 0.6339 | 0.8101 | |
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| 0.1463 | 66.0 | 231 | 0.6530 | 0.8101 | |
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| 0.1463 | 66.8571 | 234 | 0.6142 | 0.8101 | |
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| 0.1463 | 68.0 | 238 | 0.6290 | 0.8228 | |
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| 0.1287 | 68.8571 | 241 | 0.6334 | 0.8354 | |
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| 0.1287 | 70.0 | 245 | 0.8059 | 0.8101 | |
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| 0.1287 | 70.8571 | 248 | 0.7241 | 0.8481 | |
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| 0.1323 | 72.0 | 252 | 0.6836 | 0.8481 | |
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| 0.1323 | 72.8571 | 255 | 0.6588 | 0.8228 | |
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| 0.1323 | 74.0 | 259 | 0.6598 | 0.8481 | |
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| 0.1042 | 74.8571 | 262 | 0.7139 | 0.8354 | |
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| 0.1042 | 76.0 | 266 | 0.7236 | 0.8354 | |
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| 0.1042 | 76.8571 | 269 | 0.6919 | 0.8354 | |
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| 0.1106 | 78.0 | 273 | 0.6568 | 0.8354 | |
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| 0.1106 | 78.8571 | 276 | 0.6556 | 0.8481 | |
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| 0.1348 | 80.0 | 280 | 0.6612 | 0.8354 | |
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| 0.1348 | 80.8571 | 283 | 0.6686 | 0.8228 | |
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| 0.1348 | 82.0 | 287 | 0.6705 | 0.8481 | |
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| 0.1352 | 82.8571 | 290 | 0.6776 | 0.8354 | |
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| 0.1352 | 84.0 | 294 | 0.6873 | 0.8354 | |
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| 0.1352 | 84.8571 | 297 | 0.6888 | 0.8354 | |
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| 0.1226 | 85.7143 | 300 | 0.6880 | 0.8354 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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