smids_1x_deit_tiny_adamax_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: 0.7139
- Accuracy: 0.8948
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 |
|---|---|---|---|---|
| 0.5342 | 1.0 | 76 | 0.3575 | 0.8581 |
| 0.3487 | 2.0 | 152 | 0.2939 | 0.8898 |
| 0.1948 | 3.0 | 228 | 0.3052 | 0.8781 |
| 0.1604 | 4.0 | 304 | 0.3458 | 0.8798 |
| 0.0971 | 5.0 | 380 | 0.3617 | 0.8781 |
| 0.0708 | 6.0 | 456 | 0.5383 | 0.8664 |
| 0.0188 | 7.0 | 532 | 0.4843 | 0.8815 |
| 0.0379 | 8.0 | 608 | 0.6406 | 0.8848 |
| 0.0764 | 9.0 | 684 | 0.6359 | 0.8531 |
| 0.0087 | 10.0 | 760 | 0.6100 | 0.8798 |
| 0.0487 | 11.0 | 836 | 0.7711 | 0.8748 |
| 0.0123 | 12.0 | 912 | 0.6958 | 0.8815 |
| 0.011 | 13.0 | 988 | 0.7079 | 0.8781 |
| 0.0004 | 14.0 | 1064 | 0.6722 | 0.8898 |
| 0.0173 | 15.0 | 1140 | 0.7341 | 0.8698 |
| 0.0003 | 16.0 | 1216 | 0.6822 | 0.8932 |
| 0.0001 | 17.0 | 1292 | 0.6501 | 0.8881 |
| 0.0148 | 18.0 | 1368 | 0.7815 | 0.8631 |
| 0.0031 | 19.0 | 1444 | 0.7055 | 0.8831 |
| 0.0045 | 20.0 | 1520 | 0.8049 | 0.8831 |
| 0.0073 | 21.0 | 1596 | 0.7920 | 0.8715 |
| 0.0063 | 22.0 | 1672 | 0.7465 | 0.8715 |
| 0.0001 | 23.0 | 1748 | 0.8004 | 0.8781 |
| 0.0088 | 24.0 | 1824 | 0.7851 | 0.8748 |
| 0.0101 | 25.0 | 1900 | 0.7887 | 0.8831 |
| 0.0047 | 26.0 | 1976 | 0.8827 | 0.8614 |
| 0.0049 | 27.0 | 2052 | 0.7414 | 0.8881 |
| 0.0051 | 28.0 | 2128 | 0.7159 | 0.8915 |
| 0.0063 | 29.0 | 2204 | 0.6956 | 0.8815 |
| 0.0 | 30.0 | 2280 | 0.7029 | 0.8915 |
| 0.0047 | 31.0 | 2356 | 0.7051 | 0.8932 |
| 0.0086 | 32.0 | 2432 | 0.7051 | 0.8948 |
| 0.0 | 33.0 | 2508 | 0.7127 | 0.8865 |
| 0.0 | 34.0 | 2584 | 0.7124 | 0.8948 |
| 0.0109 | 35.0 | 2660 | 0.7099 | 0.8965 |
| 0.0034 | 36.0 | 2736 | 0.7067 | 0.8965 |
| 0.0066 | 37.0 | 2812 | 0.7137 | 0.8881 |
| 0.0 | 38.0 | 2888 | 0.7098 | 0.8915 |
| 0.0038 | 39.0 | 2964 | 0.7130 | 0.8932 |
| 0.0 | 40.0 | 3040 | 0.7175 | 0.8932 |
| 0.0028 | 41.0 | 3116 | 0.7128 | 0.8898 |
| 0.0 | 42.0 | 3192 | 0.7109 | 0.8915 |
| 0.0048 | 43.0 | 3268 | 0.7105 | 0.8915 |
| 0.0 | 44.0 | 3344 | 0.7293 | 0.8881 |
| 0.0 | 45.0 | 3420 | 0.7138 | 0.8932 |
| 0.0 | 46.0 | 3496 | 0.7167 | 0.8948 |
| 0.0 | 47.0 | 3572 | 0.7158 | 0.8948 |
| 0.0024 | 48.0 | 3648 | 0.7140 | 0.8948 |
| 0.0 | 49.0 | 3724 | 0.7139 | 0.8948 |
| 0.0 | 50.0 | 3800 | 0.7139 | 0.8948 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0