smids_5x_deit_tiny_sgd_00001_fold2
This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0573
- Accuracy: 0.4476
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3977 | 1.0 | 375 | 1.3189 | 0.3428 |
| 1.3124 | 2.0 | 750 | 1.2870 | 0.3411 |
| 1.2542 | 3.0 | 1125 | 1.2595 | 0.3378 |
| 1.2046 | 4.0 | 1500 | 1.2361 | 0.3478 |
| 1.2563 | 5.0 | 1875 | 1.2165 | 0.3544 |
| 1.2759 | 6.0 | 2250 | 1.1999 | 0.3561 |
| 1.1771 | 7.0 | 2625 | 1.1858 | 0.3527 |
| 1.1858 | 8.0 | 3000 | 1.1739 | 0.3710 |
| 1.1713 | 9.0 | 3375 | 1.1636 | 0.3644 |
| 1.1774 | 10.0 | 3750 | 1.1549 | 0.3760 |
| 1.1522 | 11.0 | 4125 | 1.1472 | 0.3760 |
| 1.1182 | 12.0 | 4500 | 1.1403 | 0.3744 |
| 1.1161 | 13.0 | 4875 | 1.1344 | 0.3827 |
| 1.1676 | 14.0 | 5250 | 1.1289 | 0.3827 |
| 1.1382 | 15.0 | 5625 | 1.1238 | 0.3860 |
| 1.129 | 16.0 | 6000 | 1.1191 | 0.3943 |
| 1.1144 | 17.0 | 6375 | 1.1146 | 0.3910 |
| 1.1043 | 18.0 | 6750 | 1.1105 | 0.3894 |
| 1.1008 | 19.0 | 7125 | 1.1065 | 0.3960 |
| 1.1097 | 20.0 | 7500 | 1.1028 | 0.4077 |
| 1.1084 | 21.0 | 7875 | 1.0993 | 0.4093 |
| 1.0777 | 22.0 | 8250 | 1.0960 | 0.4110 |
| 1.0857 | 23.0 | 8625 | 1.0928 | 0.4126 |
| 1.096 | 24.0 | 9000 | 1.0898 | 0.4126 |
| 1.1016 | 25.0 | 9375 | 1.0869 | 0.4176 |
| 1.0637 | 26.0 | 9750 | 1.0843 | 0.4226 |
| 1.0804 | 27.0 | 10125 | 1.0817 | 0.4226 |
| 1.0961 | 28.0 | 10500 | 1.0793 | 0.4226 |
| 1.0888 | 29.0 | 10875 | 1.0771 | 0.4293 |
| 1.0508 | 30.0 | 11250 | 1.0750 | 0.4293 |
| 1.0685 | 31.0 | 11625 | 1.0730 | 0.4326 |
| 1.1026 | 32.0 | 12000 | 1.0712 | 0.4309 |
| 1.0612 | 33.0 | 12375 | 1.0694 | 0.4359 |
| 1.0734 | 34.0 | 12750 | 1.0679 | 0.4393 |
| 1.0868 | 35.0 | 13125 | 1.0664 | 0.4393 |
| 1.0597 | 36.0 | 13500 | 1.0650 | 0.4393 |
| 1.0653 | 37.0 | 13875 | 1.0638 | 0.4409 |
| 1.0598 | 38.0 | 14250 | 1.0627 | 0.4443 |
| 1.0773 | 39.0 | 14625 | 1.0617 | 0.4443 |
| 1.0819 | 40.0 | 15000 | 1.0608 | 0.4443 |
| 1.0608 | 41.0 | 15375 | 1.0600 | 0.4459 |
| 1.0652 | 42.0 | 15750 | 1.0594 | 0.4459 |
| 1.04 | 43.0 | 16125 | 1.0588 | 0.4476 |
| 1.0518 | 44.0 | 16500 | 1.0583 | 0.4476 |
| 1.0814 | 45.0 | 16875 | 1.0580 | 0.4476 |
| 1.0536 | 46.0 | 17250 | 1.0577 | 0.4476 |
| 1.0612 | 47.0 | 17625 | 1.0575 | 0.4476 |
| 1.0833 | 48.0 | 18000 | 1.0574 | 0.4476 |
| 1.0816 | 49.0 | 18375 | 1.0573 | 0.4476 |
| 1.0754 | 50.0 | 18750 | 1.0573 | 0.4476 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2