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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-patch16-224-65-fold4 |
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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: 0.8732394366197183 |
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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-patch16-224-65-fold4 |
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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: 0.5415 |
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- Accuracy: 0.8732 |
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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 | 0.9231 | 3 | 0.7415 | 0.5352 | |
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| No log | 1.8462 | 6 | 0.7177 | 0.4507 | |
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| No log | 2.7692 | 9 | 0.6709 | 0.6056 | |
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| 0.748 | 4.0 | 13 | 0.6333 | 0.6338 | |
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| 0.748 | 4.9231 | 16 | 0.6162 | 0.7324 | |
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| 0.748 | 5.8462 | 19 | 0.6303 | 0.6338 | |
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| 0.6397 | 6.7692 | 22 | 0.5950 | 0.6761 | |
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| 0.6397 | 8.0 | 26 | 0.6325 | 0.6056 | |
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| 0.6397 | 8.9231 | 29 | 0.5799 | 0.7042 | |
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| 0.5957 | 9.8462 | 32 | 0.5793 | 0.6901 | |
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| 0.5957 | 10.7692 | 35 | 0.5869 | 0.7183 | |
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| 0.5957 | 12.0 | 39 | 0.6195 | 0.5775 | |
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| 0.5676 | 12.9231 | 42 | 0.5940 | 0.6479 | |
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| 0.5676 | 13.8462 | 45 | 0.6612 | 0.6197 | |
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| 0.5676 | 14.7692 | 48 | 0.5598 | 0.7465 | |
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| 0.5952 | 16.0 | 52 | 0.5472 | 0.7465 | |
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| 0.5952 | 16.9231 | 55 | 0.4823 | 0.7887 | |
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| 0.5952 | 17.8462 | 58 | 0.6493 | 0.6901 | |
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| 0.4908 | 18.7692 | 61 | 0.5539 | 0.7465 | |
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| 0.4908 | 20.0 | 65 | 0.5406 | 0.7606 | |
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| 0.4908 | 20.9231 | 68 | 0.5443 | 0.7606 | |
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| 0.4474 | 21.8462 | 71 | 0.6548 | 0.7042 | |
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| 0.4474 | 22.7692 | 74 | 0.4924 | 0.7746 | |
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| 0.4474 | 24.0 | 78 | 0.4671 | 0.8169 | |
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| 0.4106 | 24.9231 | 81 | 0.4117 | 0.8310 | |
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| 0.4106 | 25.8462 | 84 | 0.4630 | 0.8592 | |
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| 0.4106 | 26.7692 | 87 | 0.4915 | 0.8310 | |
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| 0.3163 | 28.0 | 91 | 0.6336 | 0.8028 | |
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| 0.3163 | 28.9231 | 94 | 0.5920 | 0.7887 | |
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| 0.3163 | 29.8462 | 97 | 0.5653 | 0.8028 | |
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| 0.3234 | 30.7692 | 100 | 0.6411 | 0.7746 | |
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| 0.3234 | 32.0 | 104 | 0.6728 | 0.7887 | |
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| 0.3234 | 32.9231 | 107 | 0.5503 | 0.8028 | |
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| 0.2969 | 33.8462 | 110 | 0.4914 | 0.8310 | |
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| 0.2969 | 34.7692 | 113 | 0.5952 | 0.8169 | |
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| 0.2969 | 36.0 | 117 | 0.7161 | 0.7746 | |
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| 0.2325 | 36.9231 | 120 | 0.6517 | 0.7746 | |
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| 0.2325 | 37.8462 | 123 | 0.5832 | 0.7887 | |
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| 0.2325 | 38.7692 | 126 | 0.6309 | 0.7746 | |
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| 0.2447 | 40.0 | 130 | 0.8011 | 0.7465 | |
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| 0.2447 | 40.9231 | 133 | 0.6085 | 0.7887 | |
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| 0.2447 | 41.8462 | 136 | 0.6470 | 0.7606 | |
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| 0.2447 | 42.7692 | 139 | 0.7744 | 0.7746 | |
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| 0.2217 | 44.0 | 143 | 0.5730 | 0.8310 | |
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| 0.2217 | 44.9231 | 146 | 0.5577 | 0.8169 | |
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| 0.2217 | 45.8462 | 149 | 0.5226 | 0.8451 | |
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| 0.2231 | 46.7692 | 152 | 0.5115 | 0.8310 | |
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| 0.2231 | 48.0 | 156 | 0.5415 | 0.8732 | |
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| 0.2231 | 48.9231 | 159 | 0.5971 | 0.8310 | |
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| 0.2014 | 49.8462 | 162 | 0.8717 | 0.7606 | |
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| 0.2014 | 50.7692 | 165 | 0.7063 | 0.7887 | |
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| 0.2014 | 52.0 | 169 | 0.6917 | 0.7887 | |
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| 0.1827 | 52.9231 | 172 | 0.6880 | 0.7887 | |
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| 0.1827 | 53.8462 | 175 | 0.7027 | 0.8028 | |
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| 0.1827 | 54.7692 | 178 | 0.6764 | 0.8310 | |
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| 0.1558 | 56.0 | 182 | 0.7398 | 0.7887 | |
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| 0.1558 | 56.9231 | 185 | 0.7787 | 0.8169 | |
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| 0.1558 | 57.8462 | 188 | 0.7678 | 0.8169 | |
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| 0.1637 | 58.7692 | 191 | 0.7898 | 0.7606 | |
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| 0.1637 | 60.0 | 195 | 0.7105 | 0.8310 | |
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| 0.1637 | 60.9231 | 198 | 0.7262 | 0.8592 | |
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| 0.1591 | 61.8462 | 201 | 0.7464 | 0.8169 | |
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| 0.1591 | 62.7692 | 204 | 0.7233 | 0.8310 | |
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| 0.1591 | 64.0 | 208 | 0.7263 | 0.8310 | |
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| 0.1521 | 64.9231 | 211 | 0.7377 | 0.8028 | |
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| 0.1521 | 65.8462 | 214 | 0.7267 | 0.8310 | |
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| 0.1521 | 66.7692 | 217 | 0.7178 | 0.8169 | |
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| 0.157 | 68.0 | 221 | 0.8585 | 0.7887 | |
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| 0.157 | 68.9231 | 224 | 0.8629 | 0.7887 | |
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| 0.157 | 69.8462 | 227 | 0.7329 | 0.8028 | |
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| 0.1593 | 70.7692 | 230 | 0.6997 | 0.8310 | |
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| 0.1593 | 72.0 | 234 | 0.8074 | 0.8028 | |
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| 0.1593 | 72.9231 | 237 | 1.0352 | 0.7887 | |
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| 0.134 | 73.8462 | 240 | 1.0472 | 0.7887 | |
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| 0.134 | 74.7692 | 243 | 0.7477 | 0.8169 | |
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| 0.134 | 76.0 | 247 | 0.7357 | 0.8310 | |
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| 0.1386 | 76.9231 | 250 | 0.8497 | 0.7887 | |
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| 0.1386 | 77.8462 | 253 | 0.9464 | 0.7746 | |
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| 0.1386 | 78.7692 | 256 | 0.8535 | 0.7887 | |
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| 0.1246 | 80.0 | 260 | 0.7998 | 0.8310 | |
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| 0.1246 | 80.9231 | 263 | 0.8214 | 0.8310 | |
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| 0.1246 | 81.8462 | 266 | 0.8374 | 0.8028 | |
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| 0.1246 | 82.7692 | 269 | 0.8597 | 0.8028 | |
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| 0.1271 | 84.0 | 273 | 0.8437 | 0.8028 | |
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| 0.1271 | 84.9231 | 276 | 0.8370 | 0.8028 | |
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| 0.1271 | 85.8462 | 279 | 0.8298 | 0.8028 | |
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| 0.1274 | 86.7692 | 282 | 0.8340 | 0.8028 | |
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| 0.1274 | 88.0 | 286 | 0.8462 | 0.8028 | |
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| 0.1274 | 88.9231 | 289 | 0.8594 | 0.8028 | |
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| 0.1251 | 89.8462 | 292 | 0.8504 | 0.8028 | |
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| 0.1251 | 90.7692 | 295 | 0.8480 | 0.8028 | |
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| 0.1251 | 92.0 | 299 | 0.8471 | 0.8028 | |
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| 0.1207 | 92.3077 | 300 | 0.8469 | 0.8028 | |
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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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