hushem_5x_deit_small_sgd_00001_fold1
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: 1.6074
- Accuracy: 0.2222
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 |
|---|---|---|---|---|
| 1.4194 | 1.0 | 27 | 1.6162 | 0.2222 |
| 1.4315 | 2.0 | 54 | 1.6158 | 0.2222 |
| 1.4532 | 3.0 | 81 | 1.6154 | 0.2222 |
| 1.4652 | 4.0 | 108 | 1.6150 | 0.2222 |
| 1.4244 | 5.0 | 135 | 1.6147 | 0.2222 |
| 1.4622 | 6.0 | 162 | 1.6143 | 0.2222 |
| 1.4528 | 7.0 | 189 | 1.6140 | 0.2222 |
| 1.4262 | 8.0 | 216 | 1.6136 | 0.2222 |
| 1.4181 | 9.0 | 243 | 1.6133 | 0.2222 |
| 1.4163 | 10.0 | 270 | 1.6130 | 0.2222 |
| 1.4463 | 11.0 | 297 | 1.6127 | 0.2222 |
| 1.4137 | 12.0 | 324 | 1.6124 | 0.2222 |
| 1.4131 | 13.0 | 351 | 1.6121 | 0.2222 |
| 1.4148 | 14.0 | 378 | 1.6118 | 0.2222 |
| 1.444 | 15.0 | 405 | 1.6115 | 0.2222 |
| 1.4135 | 16.0 | 432 | 1.6113 | 0.2222 |
| 1.4356 | 17.0 | 459 | 1.6110 | 0.2222 |
| 1.4146 | 18.0 | 486 | 1.6108 | 0.2222 |
| 1.4096 | 19.0 | 513 | 1.6105 | 0.2222 |
| 1.4038 | 20.0 | 540 | 1.6103 | 0.2222 |
| 1.3926 | 21.0 | 567 | 1.6101 | 0.2222 |
| 1.4332 | 22.0 | 594 | 1.6099 | 0.2222 |
| 1.4214 | 23.0 | 621 | 1.6097 | 0.2222 |
| 1.4083 | 24.0 | 648 | 1.6095 | 0.2222 |
| 1.4271 | 25.0 | 675 | 1.6093 | 0.2222 |
| 1.4496 | 26.0 | 702 | 1.6091 | 0.2222 |
| 1.4117 | 27.0 | 729 | 1.6090 | 0.2222 |
| 1.403 | 28.0 | 756 | 1.6088 | 0.2222 |
| 1.3913 | 29.0 | 783 | 1.6087 | 0.2222 |
| 1.4302 | 30.0 | 810 | 1.6085 | 0.2222 |
| 1.4037 | 31.0 | 837 | 1.6084 | 0.2222 |
| 1.4442 | 32.0 | 864 | 1.6083 | 0.2222 |
| 1.4272 | 33.0 | 891 | 1.6082 | 0.2222 |
| 1.4095 | 34.0 | 918 | 1.6080 | 0.2222 |
| 1.4234 | 35.0 | 945 | 1.6079 | 0.2222 |
| 1.4343 | 36.0 | 972 | 1.6079 | 0.2222 |
| 1.4253 | 37.0 | 999 | 1.6078 | 0.2222 |
| 1.4109 | 38.0 | 1026 | 1.6077 | 0.2222 |
| 1.4096 | 39.0 | 1053 | 1.6076 | 0.2222 |
| 1.3772 | 40.0 | 1080 | 1.6076 | 0.2222 |
| 1.4046 | 41.0 | 1107 | 1.6075 | 0.2222 |
| 1.384 | 42.0 | 1134 | 1.6075 | 0.2222 |
| 1.4202 | 43.0 | 1161 | 1.6075 | 0.2222 |
| 1.3963 | 44.0 | 1188 | 1.6074 | 0.2222 |
| 1.4183 | 45.0 | 1215 | 1.6074 | 0.2222 |
| 1.3888 | 46.0 | 1242 | 1.6074 | 0.2222 |
| 1.4088 | 47.0 | 1269 | 1.6074 | 0.2222 |
| 1.393 | 48.0 | 1296 | 1.6074 | 0.2222 |
| 1.4397 | 49.0 | 1323 | 1.6074 | 0.2222 |
| 1.4472 | 50.0 | 1350 | 1.6074 | 0.2222 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0