smids_1x_deit_small_sgd_0001_fold5
This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.7968
- Accuracy: 0.6783
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 |
|---|---|---|---|---|
| 1.0626 | 1.0 | 75 | 1.0608 | 0.4483 |
| 1.0568 | 2.0 | 150 | 1.0464 | 0.4867 |
| 1.0365 | 3.0 | 225 | 1.0338 | 0.505 |
| 1.0078 | 4.0 | 300 | 1.0223 | 0.5333 |
| 0.9982 | 5.0 | 375 | 1.0117 | 0.5367 |
| 0.9993 | 6.0 | 450 | 1.0016 | 0.5483 |
| 0.9996 | 7.0 | 525 | 0.9918 | 0.5533 |
| 0.9666 | 8.0 | 600 | 0.9822 | 0.5583 |
| 0.9644 | 9.0 | 675 | 0.9728 | 0.5617 |
| 0.9545 | 10.0 | 750 | 0.9636 | 0.575 |
| 0.9539 | 11.0 | 825 | 0.9547 | 0.5817 |
| 0.9365 | 12.0 | 900 | 0.9461 | 0.5867 |
| 0.9407 | 13.0 | 975 | 0.9377 | 0.5983 |
| 0.9254 | 14.0 | 1050 | 0.9295 | 0.6067 |
| 0.8947 | 15.0 | 1125 | 0.9216 | 0.61 |
| 0.8953 | 16.0 | 1200 | 0.9139 | 0.615 |
| 0.8981 | 17.0 | 1275 | 0.9064 | 0.6217 |
| 0.8879 | 18.0 | 1350 | 0.8991 | 0.625 |
| 0.8717 | 19.0 | 1425 | 0.8924 | 0.625 |
| 0.894 | 20.0 | 1500 | 0.8856 | 0.6233 |
| 0.8798 | 21.0 | 1575 | 0.8793 | 0.6267 |
| 0.8697 | 22.0 | 1650 | 0.8733 | 0.6283 |
| 0.8459 | 23.0 | 1725 | 0.8674 | 0.6283 |
| 0.8379 | 24.0 | 1800 | 0.8619 | 0.6317 |
| 0.8435 | 25.0 | 1875 | 0.8567 | 0.63 |
| 0.8249 | 26.0 | 1950 | 0.8516 | 0.6367 |
| 0.8188 | 27.0 | 2025 | 0.8468 | 0.6433 |
| 0.8401 | 28.0 | 2100 | 0.8423 | 0.645 |
| 0.8328 | 29.0 | 2175 | 0.8381 | 0.6483 |
| 0.8111 | 30.0 | 2250 | 0.8341 | 0.66 |
| 0.8031 | 31.0 | 2325 | 0.8302 | 0.6633 |
| 0.8167 | 32.0 | 2400 | 0.8267 | 0.665 |
| 0.8141 | 33.0 | 2475 | 0.8233 | 0.6667 |
| 0.7864 | 34.0 | 2550 | 0.8201 | 0.6717 |
| 0.7796 | 35.0 | 2625 | 0.8172 | 0.6717 |
| 0.767 | 36.0 | 2700 | 0.8145 | 0.675 |
| 0.759 | 37.0 | 2775 | 0.8119 | 0.675 |
| 0.7758 | 38.0 | 2850 | 0.8096 | 0.675 |
| 0.7909 | 39.0 | 2925 | 0.8075 | 0.675 |
| 0.7767 | 40.0 | 3000 | 0.8055 | 0.6767 |
| 0.7913 | 41.0 | 3075 | 0.8038 | 0.6767 |
| 0.7892 | 42.0 | 3150 | 0.8023 | 0.6783 |
| 0.787 | 43.0 | 3225 | 0.8010 | 0.6783 |
| 0.7743 | 44.0 | 3300 | 0.7998 | 0.6783 |
| 0.7658 | 45.0 | 3375 | 0.7988 | 0.6783 |
| 0.8086 | 46.0 | 3450 | 0.7980 | 0.6817 |
| 0.7797 | 47.0 | 3525 | 0.7974 | 0.68 |
| 0.7878 | 48.0 | 3600 | 0.7970 | 0.6783 |
| 0.77 | 49.0 | 3675 | 0.7968 | 0.6783 |
| 0.742 | 50.0 | 3750 | 0.7968 | 0.6783 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0