metadata
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_sgd_00001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.3883333333333333
smids_5x_beit_base_sgd_00001_fold4
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: 1.1429
- Accuracy: 0.3883
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 |
|---|---|---|---|---|
| 1.2481 | 1.0 | 375 | 1.3387 | 0.3367 |
| 1.2853 | 2.0 | 750 | 1.3297 | 0.34 |
| 1.2245 | 3.0 | 1125 | 1.3209 | 0.34 |
| 1.2269 | 4.0 | 1500 | 1.3124 | 0.34 |
| 1.1881 | 5.0 | 1875 | 1.3044 | 0.34 |
| 1.1962 | 6.0 | 2250 | 1.2965 | 0.3417 |
| 1.1766 | 7.0 | 2625 | 1.2890 | 0.345 |
| 1.1422 | 8.0 | 3000 | 1.2816 | 0.3467 |
| 1.1265 | 9.0 | 3375 | 1.2745 | 0.3483 |
| 1.1532 | 10.0 | 3750 | 1.2676 | 0.3517 |
| 1.1456 | 11.0 | 4125 | 1.2609 | 0.3533 |
| 1.1221 | 12.0 | 4500 | 1.2545 | 0.355 |
| 1.1397 | 13.0 | 4875 | 1.2483 | 0.355 |
| 1.1323 | 14.0 | 5250 | 1.2422 | 0.3583 |
| 1.1113 | 15.0 | 5625 | 1.2362 | 0.3617 |
| 1.1197 | 16.0 | 6000 | 1.2307 | 0.3633 |
| 1.1175 | 17.0 | 6375 | 1.2251 | 0.3667 |
| 1.1137 | 18.0 | 6750 | 1.2199 | 0.3683 |
| 1.1317 | 19.0 | 7125 | 1.2149 | 0.3667 |
| 1.0985 | 20.0 | 7500 | 1.2099 | 0.37 |
| 1.1037 | 21.0 | 7875 | 1.2054 | 0.37 |
| 1.1051 | 22.0 | 8250 | 1.2008 | 0.3717 |
| 1.1012 | 23.0 | 8625 | 1.1965 | 0.3717 |
| 1.0418 | 24.0 | 9000 | 1.1925 | 0.375 |
| 1.0922 | 25.0 | 9375 | 1.1886 | 0.3767 |
| 1.0809 | 26.0 | 9750 | 1.1848 | 0.3767 |
| 1.096 | 27.0 | 10125 | 1.1812 | 0.3767 |
| 1.0328 | 28.0 | 10500 | 1.1778 | 0.375 |
| 1.0501 | 29.0 | 10875 | 1.1745 | 0.3767 |
| 1.065 | 30.0 | 11250 | 1.1714 | 0.3767 |
| 1.0717 | 31.0 | 11625 | 1.1685 | 0.3783 |
| 1.104 | 32.0 | 12000 | 1.1658 | 0.3767 |
| 1.0567 | 33.0 | 12375 | 1.1632 | 0.3783 |
| 1.0632 | 34.0 | 12750 | 1.1607 | 0.3783 |
| 1.0635 | 35.0 | 13125 | 1.1585 | 0.3783 |
| 1.0477 | 36.0 | 13500 | 1.1563 | 0.3833 |
| 1.0721 | 37.0 | 13875 | 1.1544 | 0.385 |
| 1.0594 | 38.0 | 14250 | 1.1526 | 0.385 |
| 1.0484 | 39.0 | 14625 | 1.1510 | 0.3867 |
| 1.0408 | 40.0 | 15000 | 1.1495 | 0.3883 |
| 1.0421 | 41.0 | 15375 | 1.1482 | 0.39 |
| 1.0561 | 42.0 | 15750 | 1.1470 | 0.3883 |
| 1.0338 | 43.0 | 16125 | 1.1460 | 0.3883 |
| 1.0224 | 44.0 | 16500 | 1.1451 | 0.3883 |
| 1.0269 | 45.0 | 16875 | 1.1444 | 0.3883 |
| 1.0608 | 46.0 | 17250 | 1.1438 | 0.3883 |
| 1.0652 | 47.0 | 17625 | 1.1434 | 0.3883 |
| 1.0189 | 48.0 | 18000 | 1.1431 | 0.3883 |
| 1.0225 | 49.0 | 18375 | 1.1429 | 0.3883 |
| 1.0356 | 50.0 | 18750 | 1.1429 | 0.3883 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2