hushem_40x_deit_small_adamax_001_fold2
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: 3.2920
- Accuracy: 0.7333
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: 0.001
- 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 |
|---|---|---|---|---|
| 0.1775 | 1.0 | 215 | 1.6855 | 0.7111 |
| 0.1537 | 2.0 | 430 | 1.3524 | 0.7111 |
| 0.0687 | 3.0 | 645 | 2.1272 | 0.7333 |
| 0.0127 | 4.0 | 860 | 1.6443 | 0.7778 |
| 0.1338 | 5.0 | 1075 | 1.6931 | 0.7111 |
| 0.0106 | 6.0 | 1290 | 2.4757 | 0.6667 |
| 0.049 | 7.0 | 1505 | 2.6204 | 0.6889 |
| 0.0012 | 8.0 | 1720 | 1.8192 | 0.7333 |
| 0.0005 | 9.0 | 1935 | 1.7811 | 0.7556 |
| 0.0005 | 10.0 | 2150 | 2.2694 | 0.6889 |
| 0.0153 | 11.0 | 2365 | 1.6459 | 0.7333 |
| 0.0005 | 12.0 | 2580 | 1.8151 | 0.7778 |
| 0.0072 | 13.0 | 2795 | 1.9954 | 0.7556 |
| 0.0 | 14.0 | 3010 | 2.3490 | 0.7778 |
| 0.0073 | 15.0 | 3225 | 2.3310 | 0.7556 |
| 0.0002 | 16.0 | 3440 | 2.4489 | 0.6667 |
| 0.0001 | 17.0 | 3655 | 2.8003 | 0.6222 |
| 0.0 | 18.0 | 3870 | 2.6717 | 0.7333 |
| 0.0 | 19.0 | 4085 | 2.6848 | 0.7333 |
| 0.0 | 20.0 | 4300 | 2.6999 | 0.7333 |
| 0.0 | 21.0 | 4515 | 2.7166 | 0.7333 |
| 0.0 | 22.0 | 4730 | 2.7339 | 0.7333 |
| 0.0 | 23.0 | 4945 | 2.7519 | 0.7333 |
| 0.0 | 24.0 | 5160 | 2.7709 | 0.7333 |
| 0.0 | 25.0 | 5375 | 2.7907 | 0.7333 |
| 0.0 | 26.0 | 5590 | 2.8115 | 0.7333 |
| 0.0 | 27.0 | 5805 | 2.8327 | 0.7333 |
| 0.0 | 28.0 | 6020 | 2.8548 | 0.7333 |
| 0.0 | 29.0 | 6235 | 2.8773 | 0.7333 |
| 0.0 | 30.0 | 6450 | 2.9001 | 0.7333 |
| 0.0 | 31.0 | 6665 | 2.9234 | 0.7333 |
| 0.0 | 32.0 | 6880 | 2.9473 | 0.7333 |
| 0.0 | 33.0 | 7095 | 2.9712 | 0.7333 |
| 0.0 | 34.0 | 7310 | 2.9955 | 0.7333 |
| 0.0 | 35.0 | 7525 | 3.0198 | 0.7333 |
| 0.0 | 36.0 | 7740 | 3.0443 | 0.7333 |
| 0.0 | 37.0 | 7955 | 3.0682 | 0.7333 |
| 0.0 | 38.0 | 8170 | 3.0917 | 0.7333 |
| 0.0 | 39.0 | 8385 | 3.1162 | 0.7333 |
| 0.0 | 40.0 | 8600 | 3.1397 | 0.7333 |
| 0.0 | 41.0 | 8815 | 3.1619 | 0.7333 |
| 0.0 | 42.0 | 9030 | 3.1849 | 0.7333 |
| 0.0 | 43.0 | 9245 | 3.2057 | 0.7333 |
| 0.0 | 44.0 | 9460 | 3.2253 | 0.7333 |
| 0.0 | 45.0 | 9675 | 3.2434 | 0.7333 |
| 0.0 | 46.0 | 9890 | 3.2592 | 0.7333 |
| 0.0 | 47.0 | 10105 | 3.2727 | 0.7333 |
| 0.0 | 48.0 | 10320 | 3.2833 | 0.7333 |
| 0.0 | 49.0 | 10535 | 3.2902 | 0.7333 |
| 0.0 | 50.0 | 10750 | 3.2920 | 0.7333 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2