metadata
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-dmae-va-U5-42C
results: []
vit-base-patch16-224-dmae-va-U5-42C
This model is a fine-tuned version of google/vit-base-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1112
- Accuracy: 0.5667
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-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 42
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.9 | 7 | 1.4546 | 0.1333 |
| 1.5342 | 1.94 | 15 | 1.4379 | 0.1333 |
| 1.5342 | 2.97 | 23 | 1.4115 | 0.1667 |
| 1.5331 | 4.0 | 31 | 1.3787 | 0.2 |
| 1.4639 | 4.9 | 38 | 1.3513 | 0.2833 |
| 1.4639 | 5.94 | 46 | 1.3290 | 0.3333 |
| 1.4056 | 6.97 | 54 | 1.3114 | 0.3833 |
| 1.3679 | 8.0 | 62 | 1.2941 | 0.4333 |
| 1.3679 | 8.9 | 69 | 1.2827 | 0.4667 |
| 1.3387 | 9.94 | 77 | 1.2678 | 0.5 |
| 1.2992 | 10.97 | 85 | 1.2557 | 0.4667 |
| 1.2992 | 12.0 | 93 | 1.2454 | 0.4667 |
| 1.2797 | 12.9 | 100 | 1.2345 | 0.4833 |
| 1.2507 | 13.94 | 108 | 1.2215 | 0.4833 |
| 1.2507 | 14.97 | 116 | 1.2109 | 0.5 |
| 1.2337 | 16.0 | 124 | 1.2005 | 0.5 |
| 1.2337 | 16.9 | 131 | 1.1904 | 0.5 |
| 1.2076 | 17.94 | 139 | 1.1796 | 0.5167 |
| 1.1968 | 18.97 | 147 | 1.1699 | 0.5333 |
| 1.1968 | 20.0 | 155 | 1.1610 | 0.5333 |
| 1.171 | 20.9 | 162 | 1.1544 | 0.5333 |
| 1.1572 | 21.94 | 170 | 1.1476 | 0.5333 |
| 1.1572 | 22.97 | 178 | 1.1411 | 0.5333 |
| 1.1383 | 24.0 | 186 | 1.1350 | 0.5333 |
| 1.14 | 24.9 | 193 | 1.1298 | 0.5333 |
| 1.14 | 25.94 | 201 | 1.1256 | 0.55 |
| 1.1114 | 26.97 | 209 | 1.1212 | 0.55 |
| 1.1094 | 28.0 | 217 | 1.1173 | 0.55 |
| 1.1094 | 28.9 | 224 | 1.1143 | 0.55 |
| 1.0872 | 29.94 | 232 | 1.1112 | 0.5667 |
| 1.0941 | 30.97 | 240 | 1.1078 | 0.5667 |
| 1.0941 | 32.0 | 248 | 1.1054 | 0.5667 |
| 1.0882 | 32.9 | 255 | 1.1032 | 0.5667 |
| 1.0882 | 33.94 | 263 | 1.1012 | 0.5667 |
| 1.0685 | 34.97 | 271 | 1.0998 | 0.5667 |
| 1.0775 | 36.0 | 279 | 1.0988 | 0.5667 |
| 1.0775 | 36.9 | 286 | 1.0983 | 0.5667 |
| 1.0817 | 37.94 | 294 | 1.0981 | 0.5667 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2