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---
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-U5-42D
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-dmae-va-U5-42D
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0383
- Accuracy: 0.55
## 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: 0.003
- 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 | 1.0970 | 0.5167 |
| 1.3527 | 1.94 | 15 | 1.0383 | 0.55 |
| 1.3527 | 2.97 | 23 | 1.2351 | 0.4167 |
| 1.3013 | 4.0 | 31 | 1.3025 | 0.3333 |
| 1.3706 | 4.9 | 38 | 1.3800 | 0.2167 |
| 1.3706 | 5.94 | 46 | 1.4609 | 0.1833 |
| 1.4415 | 6.97 | 54 | 1.3718 | 0.4333 |
| 1.3602 | 8.0 | 62 | 1.3173 | 0.3167 |
| 1.3602 | 8.9 | 69 | 1.2827 | 0.4 |
| 1.3079 | 9.94 | 77 | 1.3167 | 0.3167 |
| 1.3247 | 10.97 | 85 | 1.2579 | 0.4 |
| 1.3247 | 12.0 | 93 | 1.3202 | 0.2 |
| 1.3102 | 12.9 | 100 | 1.2354 | 0.45 |
| 1.2807 | 13.94 | 108 | 1.3610 | 0.25 |
| 1.2807 | 14.97 | 116 | 1.2803 | 0.4 |
| 1.2774 | 16.0 | 124 | 1.3338 | 0.2167 |
| 1.2774 | 16.9 | 131 | 1.2549 | 0.35 |
| 1.2596 | 17.94 | 139 | 1.2693 | 0.3667 |
| 1.2413 | 18.97 | 147 | 1.3005 | 0.2167 |
| 1.2413 | 20.0 | 155 | 1.2299 | 0.4333 |
| 1.262 | 20.9 | 162 | 1.3454 | 0.2667 |
| 1.2261 | 21.94 | 170 | 1.2818 | 0.3167 |
| 1.2261 | 22.97 | 178 | 1.2498 | 0.4333 |
| 1.2405 | 24.0 | 186 | 1.3376 | 0.3167 |
| 1.2245 | 24.9 | 193 | 1.2595 | 0.3667 |
| 1.2245 | 25.94 | 201 | 1.3319 | 0.4 |
| 1.2034 | 26.97 | 209 | 1.2528 | 0.3833 |
| 1.1818 | 28.0 | 217 | 1.3656 | 0.3667 |
| 1.1818 | 28.9 | 224 | 1.2501 | 0.3833 |
| 1.1479 | 29.94 | 232 | 1.3241 | 0.3 |
| 1.1193 | 30.97 | 240 | 1.3803 | 0.3667 |
| 1.1193 | 32.0 | 248 | 1.2294 | 0.4167 |
| 1.1071 | 32.9 | 255 | 1.4134 | 0.5 |
| 1.1071 | 33.94 | 263 | 1.4123 | 0.3667 |
| 1.0429 | 34.97 | 271 | 1.2184 | 0.5 |
| 1.0528 | 36.0 | 279 | 1.3100 | 0.45 |
| 1.0528 | 36.9 | 286 | 1.3249 | 0.3833 |
| 1.0055 | 37.94 | 294 | 1.3051 | 0.5 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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