hushem_5x_deit_tiny_sgd_0001_fold5
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: 1.4076
- Accuracy: 0.3902
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: 0.0001
- 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 |
|---|---|---|---|---|
| 1.5266 | 1.0 | 28 | 1.7430 | 0.2439 |
| 1.4534 | 2.0 | 56 | 1.7173 | 0.2439 |
| 1.4589 | 3.0 | 84 | 1.6934 | 0.2439 |
| 1.4589 | 4.0 | 112 | 1.6722 | 0.2439 |
| 1.4564 | 5.0 | 140 | 1.6529 | 0.2439 |
| 1.475 | 6.0 | 168 | 1.6343 | 0.2439 |
| 1.4209 | 7.0 | 196 | 1.6174 | 0.2439 |
| 1.4499 | 8.0 | 224 | 1.6010 | 0.2439 |
| 1.482 | 9.0 | 252 | 1.5863 | 0.2439 |
| 1.4359 | 10.0 | 280 | 1.5742 | 0.2439 |
| 1.4148 | 11.0 | 308 | 1.5620 | 0.2439 |
| 1.4175 | 12.0 | 336 | 1.5508 | 0.2439 |
| 1.4309 | 13.0 | 364 | 1.5414 | 0.2439 |
| 1.4434 | 14.0 | 392 | 1.5314 | 0.2439 |
| 1.4301 | 15.0 | 420 | 1.5227 | 0.2195 |
| 1.4116 | 16.0 | 448 | 1.5140 | 0.2439 |
| 1.4213 | 17.0 | 476 | 1.5063 | 0.2439 |
| 1.4048 | 18.0 | 504 | 1.4983 | 0.2439 |
| 1.4279 | 19.0 | 532 | 1.4909 | 0.2439 |
| 1.422 | 20.0 | 560 | 1.4842 | 0.2683 |
| 1.3928 | 21.0 | 588 | 1.4779 | 0.2683 |
| 1.3839 | 22.0 | 616 | 1.4719 | 0.2683 |
| 1.393 | 23.0 | 644 | 1.4665 | 0.2683 |
| 1.3889 | 24.0 | 672 | 1.4611 | 0.2683 |
| 1.3853 | 25.0 | 700 | 1.4568 | 0.2927 |
| 1.349 | 26.0 | 728 | 1.4521 | 0.3171 |
| 1.3871 | 27.0 | 756 | 1.4479 | 0.3171 |
| 1.3753 | 28.0 | 784 | 1.4439 | 0.3415 |
| 1.3905 | 29.0 | 812 | 1.4406 | 0.3415 |
| 1.3675 | 30.0 | 840 | 1.4371 | 0.3415 |
| 1.3814 | 31.0 | 868 | 1.4338 | 0.3415 |
| 1.3666 | 32.0 | 896 | 1.4305 | 0.3415 |
| 1.3609 | 33.0 | 924 | 1.4275 | 0.3659 |
| 1.3514 | 34.0 | 952 | 1.4249 | 0.3902 |
| 1.3706 | 35.0 | 980 | 1.4224 | 0.3902 |
| 1.3609 | 36.0 | 1008 | 1.4204 | 0.3902 |
| 1.3195 | 37.0 | 1036 | 1.4184 | 0.3902 |
| 1.3869 | 38.0 | 1064 | 1.4165 | 0.3902 |
| 1.3586 | 39.0 | 1092 | 1.4149 | 0.3902 |
| 1.3675 | 40.0 | 1120 | 1.4134 | 0.3902 |
| 1.3299 | 41.0 | 1148 | 1.4121 | 0.3902 |
| 1.3616 | 42.0 | 1176 | 1.4109 | 0.3902 |
| 1.3801 | 43.0 | 1204 | 1.4099 | 0.3902 |
| 1.3528 | 44.0 | 1232 | 1.4091 | 0.3902 |
| 1.3348 | 45.0 | 1260 | 1.4084 | 0.3902 |
| 1.353 | 46.0 | 1288 | 1.4080 | 0.3902 |
| 1.3488 | 47.0 | 1316 | 1.4077 | 0.3902 |
| 1.3749 | 48.0 | 1344 | 1.4076 | 0.3902 |
| 1.3556 | 49.0 | 1372 | 1.4076 | 0.3902 |
| 1.3606 | 50.0 | 1400 | 1.4076 | 0.3902 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0