smids_1x_deit_tiny_rms_0001_fold1
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: 1.0693
- Accuracy: 0.8648
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 |
|---|---|---|---|---|
| 0.7287 | 1.0 | 76 | 0.8533 | 0.6394 |
| 0.5852 | 2.0 | 152 | 0.6881 | 0.7195 |
| 0.3351 | 3.0 | 228 | 0.4986 | 0.7997 |
| 0.2896 | 4.0 | 304 | 0.4784 | 0.8280 |
| 0.1989 | 5.0 | 380 | 0.4421 | 0.8664 |
| 0.1697 | 6.0 | 456 | 0.5180 | 0.8381 |
| 0.1375 | 7.0 | 532 | 0.6205 | 0.8331 |
| 0.099 | 8.0 | 608 | 0.5628 | 0.8631 |
| 0.0915 | 9.0 | 684 | 0.7872 | 0.8464 |
| 0.0302 | 10.0 | 760 | 0.6523 | 0.8748 |
| 0.067 | 11.0 | 836 | 0.7797 | 0.8447 |
| 0.0423 | 12.0 | 912 | 0.7040 | 0.8548 |
| 0.0283 | 13.0 | 988 | 0.7674 | 0.8631 |
| 0.0338 | 14.0 | 1064 | 0.7877 | 0.8564 |
| 0.0931 | 15.0 | 1140 | 0.9177 | 0.8381 |
| 0.0232 | 16.0 | 1216 | 0.9119 | 0.8531 |
| 0.0145 | 17.0 | 1292 | 0.9967 | 0.8514 |
| 0.0553 | 18.0 | 1368 | 0.9035 | 0.8531 |
| 0.0024 | 19.0 | 1444 | 0.9160 | 0.8631 |
| 0.0523 | 20.0 | 1520 | 0.8356 | 0.8648 |
| 0.033 | 21.0 | 1596 | 1.0628 | 0.8531 |
| 0.0448 | 22.0 | 1672 | 0.8647 | 0.8664 |
| 0.0183 | 23.0 | 1748 | 1.0897 | 0.8481 |
| 0.0002 | 24.0 | 1824 | 1.0415 | 0.8431 |
| 0.0163 | 25.0 | 1900 | 0.9932 | 0.8581 |
| 0.0047 | 26.0 | 1976 | 1.1496 | 0.8514 |
| 0.0072 | 27.0 | 2052 | 1.0645 | 0.8531 |
| 0.0133 | 28.0 | 2128 | 1.1427 | 0.8297 |
| 0.0062 | 29.0 | 2204 | 1.0607 | 0.8531 |
| 0.0 | 30.0 | 2280 | 0.9174 | 0.8715 |
| 0.0155 | 31.0 | 2356 | 1.0165 | 0.8598 |
| 0.0239 | 32.0 | 2432 | 1.0075 | 0.8698 |
| 0.0001 | 33.0 | 2508 | 1.0034 | 0.8731 |
| 0.0 | 34.0 | 2584 | 1.0346 | 0.8598 |
| 0.0119 | 35.0 | 2660 | 0.9934 | 0.8631 |
| 0.0037 | 36.0 | 2736 | 1.0215 | 0.8614 |
| 0.0079 | 37.0 | 2812 | 0.9915 | 0.8698 |
| 0.0 | 38.0 | 2888 | 1.0008 | 0.8698 |
| 0.0032 | 39.0 | 2964 | 1.0184 | 0.8731 |
| 0.0 | 40.0 | 3040 | 1.0021 | 0.8664 |
| 0.0033 | 41.0 | 3116 | 1.0395 | 0.8698 |
| 0.0 | 42.0 | 3192 | 1.0443 | 0.8664 |
| 0.0049 | 43.0 | 3268 | 1.0447 | 0.8681 |
| 0.0 | 44.0 | 3344 | 1.0507 | 0.8664 |
| 0.0 | 45.0 | 3420 | 1.0520 | 0.8648 |
| 0.0 | 46.0 | 3496 | 1.0585 | 0.8648 |
| 0.0 | 47.0 | 3572 | 1.0642 | 0.8648 |
| 0.0026 | 48.0 | 3648 | 1.0682 | 0.8648 |
| 0.0 | 49.0 | 3724 | 1.0688 | 0.8648 |
| 0.0 | 50.0 | 3800 | 1.0693 | 0.8648 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0