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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-patch16-224-75-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: 0.9302325581395349 |
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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-patch16-224-75-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: 0.2641 |
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- Accuracy: 0.9302 |
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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 | 1.0 | 2 | 1.0987 | 0.3023 | |
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| No log | 2.0 | 4 | 0.6630 | 0.6977 | |
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| No log | 3.0 | 6 | 0.8342 | 0.6977 | |
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| No log | 4.0 | 8 | 0.6752 | 0.6977 | |
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| 0.7768 | 5.0 | 10 | 0.5408 | 0.7209 | |
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| 0.7768 | 6.0 | 12 | 0.7252 | 0.6977 | |
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| 0.7768 | 7.0 | 14 | 0.5609 | 0.7209 | |
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| 0.7768 | 8.0 | 16 | 0.7345 | 0.6977 | |
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| 0.7768 | 9.0 | 18 | 0.4614 | 0.7674 | |
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| 0.4249 | 10.0 | 20 | 0.4434 | 0.8372 | |
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| 0.4249 | 11.0 | 22 | 0.7552 | 0.7442 | |
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| 0.4249 | 12.0 | 24 | 0.4142 | 0.7674 | |
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| 0.4249 | 13.0 | 26 | 0.7183 | 0.7442 | |
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| 0.4249 | 14.0 | 28 | 0.5591 | 0.7907 | |
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| 0.3506 | 15.0 | 30 | 0.4363 | 0.6977 | |
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| 0.3506 | 16.0 | 32 | 0.5738 | 0.7907 | |
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| 0.3506 | 17.0 | 34 | 0.4286 | 0.8140 | |
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| 0.3506 | 18.0 | 36 | 0.4200 | 0.8140 | |
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| 0.3506 | 19.0 | 38 | 0.6514 | 0.7442 | |
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| 0.3434 | 20.0 | 40 | 0.4190 | 0.7907 | |
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| 0.3434 | 21.0 | 42 | 0.6220 | 0.8140 | |
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| 0.3434 | 22.0 | 44 | 0.6334 | 0.7907 | |
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| 0.3434 | 23.0 | 46 | 0.4487 | 0.8372 | |
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| 0.3434 | 24.0 | 48 | 0.4960 | 0.8605 | |
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| 0.2498 | 25.0 | 50 | 0.4179 | 0.8605 | |
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| 0.2498 | 26.0 | 52 | 0.3221 | 0.8605 | |
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| 0.2498 | 27.0 | 54 | 0.4776 | 0.8372 | |
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| 0.2498 | 28.0 | 56 | 0.5756 | 0.8605 | |
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| 0.2498 | 29.0 | 58 | 0.5444 | 0.8372 | |
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| 0.2461 | 30.0 | 60 | 0.3973 | 0.8605 | |
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| 0.2461 | 31.0 | 62 | 0.3672 | 0.8605 | |
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| 0.2461 | 32.0 | 64 | 0.4071 | 0.8837 | |
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| 0.2461 | 33.0 | 66 | 0.4678 | 0.7674 | |
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| 0.2461 | 34.0 | 68 | 0.2641 | 0.9302 | |
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| 0.2279 | 35.0 | 70 | 0.5551 | 0.8372 | |
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| 0.2279 | 36.0 | 72 | 0.2727 | 0.9302 | |
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| 0.2279 | 37.0 | 74 | 0.3312 | 0.8837 | |
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| 0.2279 | 38.0 | 76 | 0.7485 | 0.7907 | |
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| 0.2279 | 39.0 | 78 | 0.6407 | 0.8605 | |
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| 0.183 | 40.0 | 80 | 0.5420 | 0.8372 | |
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| 0.183 | 41.0 | 82 | 0.7364 | 0.8605 | |
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| 0.183 | 42.0 | 84 | 0.4141 | 0.8605 | |
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| 0.183 | 43.0 | 86 | 0.5461 | 0.7907 | |
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| 0.183 | 44.0 | 88 | 0.3438 | 0.8605 | |
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| 0.1658 | 45.0 | 90 | 0.3322 | 0.9302 | |
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| 0.1658 | 46.0 | 92 | 0.3463 | 0.9302 | |
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| 0.1658 | 47.0 | 94 | 0.6066 | 0.8605 | |
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| 0.1658 | 48.0 | 96 | 0.6259 | 0.8605 | |
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| 0.1658 | 49.0 | 98 | 0.4909 | 0.8372 | |
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| 0.1555 | 50.0 | 100 | 0.6022 | 0.7907 | |
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| 0.1555 | 51.0 | 102 | 0.5234 | 0.8372 | |
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| 0.1555 | 52.0 | 104 | 0.4164 | 0.8837 | |
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| 0.1555 | 53.0 | 106 | 0.3893 | 0.8605 | |
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| 0.1555 | 54.0 | 108 | 0.3774 | 0.8837 | |
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| 0.1487 | 55.0 | 110 | 0.7532 | 0.8372 | |
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| 0.1487 | 56.0 | 112 | 0.7141 | 0.8605 | |
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| 0.1487 | 57.0 | 114 | 0.4197 | 0.9070 | |
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| 0.1487 | 58.0 | 116 | 0.6816 | 0.7442 | |
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| 0.1487 | 59.0 | 118 | 0.5384 | 0.8140 | |
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| 0.1349 | 60.0 | 120 | 0.4971 | 0.8605 | |
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| 0.1349 | 61.0 | 122 | 0.4601 | 0.8837 | |
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| 0.1349 | 62.0 | 124 | 0.4740 | 0.8372 | |
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| 0.1349 | 63.0 | 126 | 0.5386 | 0.8140 | |
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| 0.1349 | 64.0 | 128 | 0.3376 | 0.9070 | |
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| 0.128 | 65.0 | 130 | 0.3905 | 0.9070 | |
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| 0.128 | 66.0 | 132 | 0.3841 | 0.9302 | |
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| 0.128 | 67.0 | 134 | 0.3567 | 0.8605 | |
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| 0.128 | 68.0 | 136 | 0.3985 | 0.8372 | |
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| 0.128 | 69.0 | 138 | 0.4165 | 0.8372 | |
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| 0.0875 | 70.0 | 140 | 0.4346 | 0.8605 | |
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| 0.0875 | 71.0 | 142 | 0.4497 | 0.8372 | |
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| 0.0875 | 72.0 | 144 | 0.4353 | 0.8837 | |
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| 0.0875 | 73.0 | 146 | 0.4276 | 0.8837 | |
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| 0.0875 | 74.0 | 148 | 0.4010 | 0.8837 | |
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| 0.0932 | 75.0 | 150 | 0.3958 | 0.9070 | |
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| 0.0932 | 76.0 | 152 | 0.3604 | 0.9070 | |
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| 0.0932 | 77.0 | 154 | 0.3427 | 0.8837 | |
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| 0.0932 | 78.0 | 156 | 0.3417 | 0.8837 | |
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| 0.0932 | 79.0 | 158 | 0.3438 | 0.9070 | |
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| 0.0943 | 80.0 | 160 | 0.3756 | 0.9302 | |
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| 0.0943 | 81.0 | 162 | 0.4077 | 0.9302 | |
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| 0.0943 | 82.0 | 164 | 0.4129 | 0.9302 | |
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| 0.0943 | 83.0 | 166 | 0.4304 | 0.9302 | |
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| 0.0943 | 84.0 | 168 | 0.4156 | 0.9302 | |
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| 0.0753 | 85.0 | 170 | 0.4088 | 0.9070 | |
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| 0.0753 | 86.0 | 172 | 0.4090 | 0.8837 | |
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| 0.0753 | 87.0 | 174 | 0.4076 | 0.9070 | |
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| 0.0753 | 88.0 | 176 | 0.4273 | 0.9070 | |
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| 0.0753 | 89.0 | 178 | 0.4367 | 0.9070 | |
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| 0.0846 | 90.0 | 180 | 0.4490 | 0.9070 | |
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| 0.0846 | 91.0 | 182 | 0.4448 | 0.8837 | |
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| 0.0846 | 92.0 | 184 | 0.4406 | 0.8837 | |
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| 0.0846 | 93.0 | 186 | 0.4393 | 0.8837 | |
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| 0.0846 | 94.0 | 188 | 0.4370 | 0.8837 | |
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| 0.0865 | 95.0 | 190 | 0.4330 | 0.8837 | |
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| 0.0865 | 96.0 | 192 | 0.4293 | 0.8837 | |
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| 0.0865 | 97.0 | 194 | 0.4240 | 0.8837 | |
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| 0.0865 | 98.0 | 196 | 0.4177 | 0.8837 | |
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| 0.0865 | 99.0 | 198 | 0.4144 | 0.8837 | |
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| 0.1019 | 100.0 | 200 | 0.4135 | 0.8837 | |
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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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