hushem_1x_deit_small_sgd_00001_fold3
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: 1.5939
- Accuracy: 0.1860
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 |
|---|---|---|---|---|
| No log | 1.0 | 6 | 1.6009 | 0.1860 |
| 1.5096 | 2.0 | 12 | 1.6005 | 0.1860 |
| 1.5096 | 3.0 | 18 | 1.6002 | 0.1860 |
| 1.4896 | 4.0 | 24 | 1.5998 | 0.1860 |
| 1.4946 | 5.0 | 30 | 1.5995 | 0.1860 |
| 1.4946 | 6.0 | 36 | 1.5992 | 0.1860 |
| 1.508 | 7.0 | 42 | 1.5988 | 0.1860 |
| 1.508 | 8.0 | 48 | 1.5986 | 0.1860 |
| 1.4945 | 9.0 | 54 | 1.5983 | 0.1860 |
| 1.5205 | 10.0 | 60 | 1.5980 | 0.1860 |
| 1.5205 | 11.0 | 66 | 1.5977 | 0.1860 |
| 1.5058 | 12.0 | 72 | 1.5975 | 0.1860 |
| 1.5058 | 13.0 | 78 | 1.5972 | 0.1860 |
| 1.5082 | 14.0 | 84 | 1.5970 | 0.1860 |
| 1.502 | 15.0 | 90 | 1.5967 | 0.1860 |
| 1.502 | 16.0 | 96 | 1.5965 | 0.1860 |
| 1.5281 | 17.0 | 102 | 1.5963 | 0.1860 |
| 1.5281 | 18.0 | 108 | 1.5961 | 0.1860 |
| 1.4713 | 19.0 | 114 | 1.5959 | 0.1860 |
| 1.5067 | 20.0 | 120 | 1.5957 | 0.1860 |
| 1.5067 | 21.0 | 126 | 1.5955 | 0.1860 |
| 1.5046 | 22.0 | 132 | 1.5953 | 0.1860 |
| 1.5046 | 23.0 | 138 | 1.5952 | 0.1860 |
| 1.4884 | 24.0 | 144 | 1.5950 | 0.1860 |
| 1.4923 | 25.0 | 150 | 1.5949 | 0.1860 |
| 1.4923 | 26.0 | 156 | 1.5948 | 0.1860 |
| 1.4973 | 27.0 | 162 | 1.5947 | 0.1860 |
| 1.4973 | 28.0 | 168 | 1.5945 | 0.1860 |
| 1.5002 | 29.0 | 174 | 1.5945 | 0.1860 |
| 1.4807 | 30.0 | 180 | 1.5944 | 0.1860 |
| 1.4807 | 31.0 | 186 | 1.5943 | 0.1860 |
| 1.486 | 32.0 | 192 | 1.5942 | 0.1860 |
| 1.486 | 33.0 | 198 | 1.5941 | 0.1860 |
| 1.4927 | 34.0 | 204 | 1.5941 | 0.1860 |
| 1.4875 | 35.0 | 210 | 1.5940 | 0.1860 |
| 1.4875 | 36.0 | 216 | 1.5940 | 0.1860 |
| 1.5166 | 37.0 | 222 | 1.5940 | 0.1860 |
| 1.5166 | 38.0 | 228 | 1.5939 | 0.1860 |
| 1.5127 | 39.0 | 234 | 1.5939 | 0.1860 |
| 1.4974 | 40.0 | 240 | 1.5939 | 0.1860 |
| 1.4974 | 41.0 | 246 | 1.5939 | 0.1860 |
| 1.4716 | 42.0 | 252 | 1.5939 | 0.1860 |
| 1.4716 | 43.0 | 258 | 1.5939 | 0.1860 |
| 1.5277 | 44.0 | 264 | 1.5939 | 0.1860 |
| 1.501 | 45.0 | 270 | 1.5939 | 0.1860 |
| 1.501 | 46.0 | 276 | 1.5939 | 0.1860 |
| 1.4805 | 47.0 | 282 | 1.5939 | 0.1860 |
| 1.4805 | 48.0 | 288 | 1.5939 | 0.1860 |
| 1.5052 | 49.0 | 294 | 1.5939 | 0.1860 |
| 1.536 | 50.0 | 300 | 1.5939 | 0.1860 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1