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--- |
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license: apache-2.0 |
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base_model: google/vit-large-patch32-384 |
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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: ViTL-32-384-1e4-batch_16_epoch_4_classes_24 |
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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: 0.9755747126436781 |
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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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# ViTL-32-384-1e4-batch_16_epoch_4_classes_24 |
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This model is a fine-tuned version of [google/vit-large-patch32-384](https://huggingface.co/google/vit-large-patch32-384) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1157 |
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- Accuracy: 0.9756 |
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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: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 3 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.3336 | 0.03 | 100 | 0.2980 | 0.9325 | |
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| 0.0235 | 0.07 | 200 | 0.1580 | 0.9612 | |
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| 0.0381 | 0.1 | 300 | 0.2212 | 0.9540 | |
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| 0.0507 | 0.14 | 400 | 0.4664 | 0.9037 | |
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| 0.0052 | 0.17 | 500 | 0.1737 | 0.9670 | |
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| 0.0499 | 0.21 | 600 | 0.2187 | 0.9511 | |
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| 0.0454 | 0.24 | 700 | 0.1837 | 0.9569 | |
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| 0.0317 | 0.28 | 800 | 0.2616 | 0.9497 | |
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| 0.0594 | 0.31 | 900 | 0.1867 | 0.9555 | |
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| 0.0583 | 0.35 | 1000 | 0.1817 | 0.9569 | |
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| 0.0044 | 0.38 | 1100 | 0.2358 | 0.9497 | |
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| 0.0836 | 0.42 | 1200 | 0.2422 | 0.9454 | |
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| 0.0712 | 0.45 | 1300 | 0.1943 | 0.9555 | |
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| 0.0399 | 0.49 | 1400 | 0.2922 | 0.9440 | |
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| 0.0098 | 0.52 | 1500 | 0.3783 | 0.9325 | |
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| 0.0414 | 0.56 | 1600 | 0.2583 | 0.9454 | |
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| 0.1085 | 0.59 | 1700 | 0.2241 | 0.9511 | |
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| 0.0492 | 0.63 | 1800 | 0.2813 | 0.9368 | |
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| 0.044 | 0.66 | 1900 | 0.3361 | 0.9353 | |
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| 0.0344 | 0.7 | 2000 | 0.2549 | 0.9468 | |
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| 0.002 | 0.73 | 2100 | 0.1794 | 0.9641 | |
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| 0.0731 | 0.77 | 2200 | 0.2300 | 0.9540 | |
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| 0.0151 | 0.8 | 2300 | 0.2050 | 0.9569 | |
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| 0.0031 | 0.84 | 2400 | 0.2175 | 0.9454 | |
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| 0.1015 | 0.87 | 2500 | 0.1725 | 0.9626 | |
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| 0.0383 | 0.91 | 2600 | 0.2104 | 0.9540 | |
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| 0.0926 | 0.94 | 2700 | 0.1762 | 0.9540 | |
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| 0.0001 | 0.98 | 2800 | 0.1978 | 0.9612 | |
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| 0.1365 | 1.01 | 2900 | 0.1512 | 0.9655 | |
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| 0.083 | 1.04 | 3000 | 0.1298 | 0.9641 | |
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| 0.0002 | 1.08 | 3100 | 0.1976 | 0.9540 | |
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| 0.0042 | 1.11 | 3200 | 0.1719 | 0.9698 | |
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| 0.0002 | 1.15 | 3300 | 0.1924 | 0.9583 | |
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| 0.0002 | 1.18 | 3400 | 0.1732 | 0.9626 | |
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| 0.0978 | 1.22 | 3500 | 0.1902 | 0.9612 | |
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| 0.1067 | 1.25 | 3600 | 0.1868 | 0.9612 | |
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| 0.0005 | 1.29 | 3700 | 0.2166 | 0.9468 | |
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| 0.0007 | 1.32 | 3800 | 0.2293 | 0.9425 | |
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| 0.0001 | 1.36 | 3900 | 0.2296 | 0.9626 | |
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| 0.0001 | 1.39 | 4000 | 0.1685 | 0.9684 | |
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| 0.0001 | 1.43 | 4100 | 0.2106 | 0.9655 | |
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| 0.0004 | 1.46 | 4200 | 0.1614 | 0.9670 | |
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| 0.0 | 1.5 | 4300 | 0.1311 | 0.9727 | |
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| 0.0 | 1.53 | 4400 | 0.1445 | 0.9784 | |
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| 0.0433 | 1.57 | 4500 | 0.1544 | 0.9727 | |
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| 0.0263 | 1.6 | 4600 | 0.2133 | 0.9626 | |
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| 0.0 | 1.64 | 4700 | 0.1903 | 0.9598 | |
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| 0.0 | 1.67 | 4800 | 0.1587 | 0.9583 | |
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| 0.0 | 1.71 | 4900 | 0.1817 | 0.9655 | |
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| 0.1503 | 1.74 | 5000 | 0.2346 | 0.9526 | |
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| 0.0699 | 1.78 | 5100 | 0.1143 | 0.9713 | |
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| 0.0004 | 1.81 | 5200 | 0.1937 | 0.9626 | |
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| 0.0001 | 1.85 | 5300 | 0.2660 | 0.9540 | |
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| 0.2208 | 1.88 | 5400 | 0.1500 | 0.9713 | |
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| 0.0494 | 1.92 | 5500 | 0.1203 | 0.9698 | |
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| 0.0001 | 1.95 | 5600 | 0.1231 | 0.9756 | |
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| 0.0001 | 1.99 | 5700 | 0.1254 | 0.9698 | |
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| 0.0 | 2.02 | 5800 | 0.1622 | 0.9684 | |
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| 0.0001 | 2.06 | 5900 | 0.1464 | 0.9698 | |
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| 0.0 | 2.09 | 6000 | 0.1420 | 0.9698 | |
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| 0.0 | 2.12 | 6100 | 0.1416 | 0.9698 | |
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| 0.0 | 2.16 | 6200 | 0.1408 | 0.9698 | |
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| 0.0001 | 2.19 | 6300 | 0.1402 | 0.9698 | |
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| 0.0147 | 2.23 | 6400 | 0.1536 | 0.9655 | |
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| 0.0 | 2.26 | 6500 | 0.1944 | 0.9612 | |
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| 0.0 | 2.3 | 6600 | 0.1724 | 0.9684 | |
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| 0.0003 | 2.33 | 6700 | 0.1910 | 0.9612 | |
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| 0.0003 | 2.37 | 6800 | 0.1995 | 0.9626 | |
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| 0.0004 | 2.4 | 6900 | 0.1563 | 0.9655 | |
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| 0.0 | 2.44 | 7000 | 0.1460 | 0.9727 | |
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| 0.0 | 2.47 | 7100 | 0.1434 | 0.9727 | |
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| 0.0 | 2.51 | 7200 | 0.1242 | 0.9741 | |
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| 0.0041 | 2.54 | 7300 | 0.1364 | 0.9713 | |
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| 0.0 | 2.58 | 7400 | 0.1396 | 0.9684 | |
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| 0.0 | 2.61 | 7500 | 0.1371 | 0.9655 | |
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| 0.0 | 2.65 | 7600 | 0.1373 | 0.9684 | |
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| 0.0 | 2.68 | 7700 | 0.1230 | 0.9698 | |
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| 0.0 | 2.72 | 7800 | 0.1225 | 0.9698 | |
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| 0.0 | 2.75 | 7900 | 0.1223 | 0.9698 | |
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| 0.0001 | 2.79 | 8000 | 0.1218 | 0.9698 | |
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| 0.0 | 2.82 | 8100 | 0.1186 | 0.9756 | |
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| 0.0 | 2.86 | 8200 | 0.1183 | 0.9756 | |
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| 0.0 | 2.89 | 8300 | 0.1167 | 0.9756 | |
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| 0.0 | 2.93 | 8400 | 0.1163 | 0.9756 | |
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| 0.0 | 2.96 | 8500 | 0.1162 | 0.9756 | |
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| 0.0 | 3.0 | 8600 | 0.1157 | 0.9756 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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