vit-base-patch16-224-Trial006_007_008-YEL_STEM3
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: 0.0514
- Accuracy: 1.0
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: 5e-05
- train_batch_size: 60
- eval_batch_size: 60
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 240
- 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 |
|---|---|---|---|---|
| 0.7488 | 0.8 | 3 | 0.7067 | 0.5253 |
| 0.6693 | 1.87 | 7 | 0.6195 | 0.6263 |
| 0.5816 | 2.93 | 11 | 0.5114 | 0.7273 |
| 0.4669 | 4.0 | 15 | 0.3586 | 0.8990 |
| 0.3998 | 4.8 | 18 | 0.2867 | 0.9192 |
| 0.3579 | 5.87 | 22 | 0.2326 | 0.9596 |
| 0.3087 | 6.93 | 26 | 0.1684 | 0.9596 |
| 0.2448 | 8.0 | 30 | 0.1525 | 0.9697 |
| 0.207 | 8.8 | 33 | 0.1863 | 0.9394 |
| 0.2283 | 9.87 | 37 | 0.1259 | 0.9697 |
| 0.2354 | 10.93 | 41 | 0.1090 | 0.9495 |
| 0.2452 | 12.0 | 45 | 0.0999 | 0.9596 |
| 0.2373 | 12.8 | 48 | 0.0702 | 0.9899 |
| 0.1516 | 13.87 | 52 | 0.0749 | 0.9697 |
| 0.1893 | 14.93 | 56 | 0.0808 | 0.9798 |
| 0.1761 | 16.0 | 60 | 0.0514 | 1.0 |
| 0.1971 | 16.8 | 63 | 0.0954 | 0.9798 |
| 0.1801 | 17.87 | 67 | 0.0536 | 0.9899 |
| 0.2004 | 18.93 | 71 | 0.0836 | 0.9596 |
| 0.1899 | 20.0 | 75 | 0.0500 | 0.9899 |
| 0.2222 | 20.8 | 78 | 0.0874 | 0.9798 |
| 0.1341 | 21.87 | 82 | 0.0371 | 1.0 |
| 0.2187 | 22.93 | 86 | 0.0545 | 1.0 |
| 0.1547 | 24.0 | 90 | 0.0445 | 1.0 |
| 0.1307 | 24.8 | 93 | 0.0404 | 1.0 |
| 0.1563 | 25.87 | 97 | 0.0377 | 1.0 |
| 0.1467 | 26.93 | 101 | 0.0405 | 1.0 |
| 0.1413 | 28.0 | 105 | 0.0379 | 1.0 |
| 0.1317 | 28.8 | 108 | 0.0367 | 1.0 |
| 0.1264 | 29.87 | 112 | 0.0359 | 1.0 |
| 0.128 | 30.93 | 116 | 0.0328 | 1.0 |
| 0.1292 | 32.0 | 120 | 0.0356 | 1.0 |
| 0.1549 | 32.8 | 123 | 0.0339 | 1.0 |
| 0.1272 | 33.87 | 127 | 0.0296 | 1.0 |
| 0.1376 | 34.93 | 131 | 0.0295 | 1.0 |
| 0.1884 | 36.0 | 135 | 0.0308 | 1.0 |
| 0.1536 | 36.8 | 138 | 0.0295 | 1.0 |
| 0.166 | 37.87 | 142 | 0.0299 | 1.0 |
| 0.0929 | 38.93 | 146 | 0.0307 | 1.0 |
| 0.1592 | 40.0 | 150 | 0.0307 | 1.0 |
Framework versions
- Transformers 4.30.0.dev0
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.1
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Evaluation results
- Accuracy on imagefolderself-reported1.000