smids_5x_beit_base_adamax_00001_fold5
This model is a fine-tuned version of microsoft/beit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.7406
- Accuracy: 0.9083
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 |
|---|---|---|---|---|
| 0.2292 | 1.0 | 375 | 0.3140 | 0.8683 |
| 0.2092 | 2.0 | 750 | 0.2336 | 0.9067 |
| 0.1226 | 3.0 | 1125 | 0.2477 | 0.9117 |
| 0.0876 | 4.0 | 1500 | 0.2419 | 0.92 |
| 0.0679 | 5.0 | 1875 | 0.3315 | 0.8983 |
| 0.0323 | 6.0 | 2250 | 0.3576 | 0.905 |
| 0.0879 | 7.0 | 2625 | 0.4064 | 0.91 |
| 0.0394 | 8.0 | 3000 | 0.4497 | 0.91 |
| 0.0359 | 9.0 | 3375 | 0.4692 | 0.915 |
| 0.0158 | 10.0 | 3750 | 0.5082 | 0.9117 |
| 0.0543 | 11.0 | 4125 | 0.4628 | 0.92 |
| 0.0223 | 12.0 | 4500 | 0.5427 | 0.915 |
| 0.0015 | 13.0 | 4875 | 0.6163 | 0.905 |
| 0.0283 | 14.0 | 5250 | 0.6260 | 0.91 |
| 0.0146 | 15.0 | 5625 | 0.5842 | 0.9083 |
| 0.009 | 16.0 | 6000 | 0.6137 | 0.91 |
| 0.0335 | 17.0 | 6375 | 0.6438 | 0.905 |
| 0.0046 | 18.0 | 6750 | 0.6182 | 0.91 |
| 0.0123 | 19.0 | 7125 | 0.7222 | 0.8983 |
| 0.0002 | 20.0 | 7500 | 0.6977 | 0.9067 |
| 0.0066 | 21.0 | 7875 | 0.7063 | 0.915 |
| 0.0363 | 22.0 | 8250 | 0.6479 | 0.9083 |
| 0.0494 | 23.0 | 8625 | 0.6537 | 0.905 |
| 0.0006 | 24.0 | 9000 | 0.7370 | 0.9067 |
| 0.0292 | 25.0 | 9375 | 0.6958 | 0.9 |
| 0.0254 | 26.0 | 9750 | 0.7175 | 0.8983 |
| 0.0001 | 27.0 | 10125 | 0.7650 | 0.9017 |
| 0.0095 | 28.0 | 10500 | 0.7329 | 0.9067 |
| 0.0232 | 29.0 | 10875 | 0.6879 | 0.9033 |
| 0.0172 | 30.0 | 11250 | 0.7353 | 0.905 |
| 0.0378 | 31.0 | 11625 | 0.7667 | 0.9117 |
| 0.0068 | 32.0 | 12000 | 0.7535 | 0.9083 |
| 0.0003 | 33.0 | 12375 | 0.7493 | 0.9133 |
| 0.0005 | 34.0 | 12750 | 0.7396 | 0.9067 |
| 0.013 | 35.0 | 13125 | 0.7540 | 0.9083 |
| 0.0005 | 36.0 | 13500 | 0.7288 | 0.9083 |
| 0.0073 | 37.0 | 13875 | 0.7577 | 0.9067 |
| 0.0001 | 38.0 | 14250 | 0.7421 | 0.9033 |
| 0.0156 | 39.0 | 14625 | 0.7370 | 0.9067 |
| 0.0137 | 40.0 | 15000 | 0.7555 | 0.905 |
| 0.0 | 41.0 | 15375 | 0.7434 | 0.905 |
| 0.0 | 42.0 | 15750 | 0.7571 | 0.9017 |
| 0.0002 | 43.0 | 16125 | 0.7615 | 0.9033 |
| 0.0008 | 44.0 | 16500 | 0.7655 | 0.9033 |
| 0.0 | 45.0 | 16875 | 0.7629 | 0.905 |
| 0.0005 | 46.0 | 17250 | 0.7484 | 0.9117 |
| 0.0132 | 47.0 | 17625 | 0.7394 | 0.9083 |
| 0.0 | 48.0 | 18000 | 0.7491 | 0.91 |
| 0.0001 | 49.0 | 18375 | 0.7435 | 0.91 |
| 0.017 | 50.0 | 18750 | 0.7406 | 0.9083 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2