vit-base-patch16-224-ve-U13b-80R
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.4109
- Accuracy: 0.8913
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: 5.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3158 | 0.99 | 51 | 1.2967 | 0.3478 |
| 1.0472 | 2.0 | 103 | 0.9694 | 0.5 |
| 0.6641 | 2.99 | 154 | 0.7911 | 0.7391 |
| 0.5615 | 4.0 | 206 | 0.6850 | 0.7391 |
| 0.3458 | 4.99 | 257 | 0.4109 | 0.8913 |
| 0.3156 | 6.0 | 309 | 0.5213 | 0.8043 |
| 0.141 | 6.99 | 360 | 0.4793 | 0.8478 |
| 0.2016 | 8.0 | 412 | 0.6031 | 0.7826 |
| 0.2444 | 8.99 | 463 | 0.7324 | 0.8043 |
| 0.1501 | 10.0 | 515 | 0.6392 | 0.8043 |
| 0.1256 | 10.99 | 566 | 0.9706 | 0.7826 |
| 0.2421 | 12.0 | 618 | 0.8059 | 0.7826 |
| 0.103 | 12.99 | 669 | 0.7601 | 0.8478 |
| 0.1353 | 14.0 | 721 | 1.1986 | 0.7391 |
| 0.1095 | 14.99 | 772 | 1.0279 | 0.7609 |
| 0.065 | 16.0 | 824 | 1.2043 | 0.6957 |
| 0.1777 | 16.99 | 875 | 0.9779 | 0.8043 |
| 0.0813 | 18.0 | 927 | 1.3356 | 0.7391 |
| 0.2552 | 18.99 | 978 | 0.8483 | 0.8261 |
| 0.0941 | 20.0 | 1030 | 0.7106 | 0.8696 |
| 0.0486 | 20.99 | 1081 | 0.8359 | 0.8261 |
| 0.0361 | 22.0 | 1133 | 0.8710 | 0.8261 |
| 0.0361 | 22.99 | 1184 | 1.0301 | 0.8043 |
| 0.0136 | 24.0 | 1236 | 0.9015 | 0.8261 |
| 0.1441 | 24.99 | 1287 | 0.9958 | 0.8043 |
| 0.0181 | 26.0 | 1339 | 1.0793 | 0.7826 |
| 0.0612 | 26.99 | 1390 | 0.9678 | 0.8043 |
| 0.0814 | 28.0 | 1442 | 1.0320 | 0.7826 |
| 0.0479 | 28.99 | 1493 | 1.1845 | 0.7826 |
| 0.06 | 30.0 | 1545 | 1.2026 | 0.7826 |
| 0.0777 | 30.99 | 1596 | 1.1574 | 0.7826 |
| 0.0747 | 32.0 | 1648 | 1.3104 | 0.7609 |
| 0.0181 | 32.99 | 1699 | 1.1145 | 0.8043 |
| 0.0652 | 34.0 | 1751 | 1.1691 | 0.8043 |
| 0.0242 | 34.99 | 1802 | 1.2415 | 0.8043 |
| 0.0043 | 36.0 | 1854 | 1.1841 | 0.7826 |
| 0.0318 | 36.99 | 1905 | 1.2475 | 0.8043 |
| 0.0092 | 38.0 | 1957 | 1.2452 | 0.8043 |
| 0.0194 | 38.99 | 2008 | 1.2395 | 0.8043 |
| 0.0376 | 39.61 | 2040 | 1.2345 | 0.8043 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0