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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-base-cifar10 |
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results: [] |
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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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# vit-base-cifar10 |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0803 |
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- Accuracy: 0.9773 |
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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: 0.0002 |
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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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- 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: 2 |
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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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| 0.1043 | 0.0457 | 100 | 0.2855 | 0.919 | |
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| 0.2671 | 0.0914 | 200 | 0.3650 | 0.9015 | |
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| 0.2935 | 0.1371 | 300 | 0.3167 | 0.9067 | |
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| 0.27 | 0.1828 | 400 | 0.3518 | 0.8922 | |
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| 0.3634 | 0.2285 | 500 | 0.3660 | 0.8953 | |
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| 0.2559 | 0.2742 | 600 | 0.3964 | 0.8901 | |
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| 0.197 | 0.3199 | 700 | 0.2481 | 0.9253 | |
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| 0.2594 | 0.3656 | 800 | 0.2486 | 0.923 | |
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| 0.4545 | 0.4113 | 900 | 0.3271 | 0.9 | |
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| 0.1243 | 0.4570 | 1000 | 0.2448 | 0.9269 | |
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| 0.3593 | 0.5027 | 1100 | 0.2118 | 0.9354 | |
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| 0.1375 | 0.5484 | 1200 | 0.2205 | 0.9349 | |
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| 0.1521 | 0.5941 | 1300 | 0.2009 | 0.9376 | |
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| 0.1237 | 0.6399 | 1400 | 0.1803 | 0.9445 | |
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| 0.2214 | 0.6856 | 1500 | 0.2026 | 0.9395 | |
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| 0.1324 | 0.7313 | 1600 | 0.1635 | 0.9493 | |
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| 0.1864 | 0.7770 | 1700 | 0.1672 | 0.9493 | |
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| 0.128 | 0.8227 | 1800 | 0.2015 | 0.9409 | |
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| 0.121 | 0.8684 | 1900 | 0.1753 | 0.9451 | |
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| 0.1918 | 0.9141 | 2000 | 0.1370 | 0.9588 | |
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| 0.1658 | 0.9598 | 2100 | 0.1543 | 0.9535 | |
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| 0.1088 | 1.0055 | 2200 | 0.1361 | 0.9577 | |
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| 0.0916 | 1.0512 | 2300 | 0.1393 | 0.9597 | |
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| 0.005 | 1.0969 | 2400 | 0.1295 | 0.9621 | |
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| 0.0294 | 1.1426 | 2500 | 0.1327 | 0.9639 | |
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| 0.0939 | 1.1883 | 2600 | 0.1409 | 0.9621 | |
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| 0.0756 | 1.2340 | 2700 | 0.1202 | 0.9682 | |
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| 0.0466 | 1.2797 | 2800 | 0.1274 | 0.964 | |
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| 0.0565 | 1.3254 | 2900 | 0.1250 | 0.9663 | |
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| 0.0609 | 1.3711 | 3000 | 0.1299 | 0.9657 | |
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| 0.0201 | 1.4168 | 3100 | 0.1203 | 0.9685 | |
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| 0.0258 | 1.4625 | 3200 | 0.1166 | 0.9693 | |
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| 0.0913 | 1.5082 | 3300 | 0.1009 | 0.9736 | |
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| 0.0235 | 1.5539 | 3400 | 0.0964 | 0.9732 | |
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| 0.0089 | 1.5996 | 3500 | 0.0966 | 0.9747 | |
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| 0.0455 | 1.6453 | 3600 | 0.0963 | 0.9748 | |
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| 0.0271 | 1.6910 | 3700 | 0.0874 | 0.9763 | |
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| 0.0407 | 1.7367 | 3800 | 0.0898 | 0.9761 | |
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| 0.1095 | 1.7824 | 3900 | 0.0849 | 0.976 | |
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| 0.0327 | 1.8282 | 4000 | 0.0926 | 0.9745 | |
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| 0.0427 | 1.8739 | 4100 | 0.0811 | 0.9769 | |
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| 0.003 | 1.9196 | 4200 | 0.0821 | 0.9761 | |
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| 0.0182 | 1.9653 | 4300 | 0.0803 | 0.9773 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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