File size: 8,046 Bytes
9c3035b 981f17d 9c3035b 981f17d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224
results: []
datasets:
- corranm/first_vote_100_full_pic_without_vote_highlight_square
---
<!-- 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
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3122
- F1 Macro: 0.5397
- F1 Micro: 0.6212
- F1 Weighted: 0.6077
- Precision Macro: 0.5343
- Precision Micro: 0.6212
- Precision Weighted: 0.6084
- Recall Macro: 0.5571
- Recall Micro: 0.6212
- Recall Weighted: 0.6212
- Accuracy: 0.6212
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | F1 Weighted | Precision Macro | Precision Micro | Precision Weighted | Recall Macro | Recall Micro | Recall Weighted | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.9037 | 1.0 | 29 | 1.8618 | 0.1250 | 0.2197 | 0.1592 | 0.1401 | 0.2197 | 0.1692 | 0.1674 | 0.2197 | 0.2197 | 0.2197 |
| 1.6981 | 2.0 | 58 | 1.8760 | 0.1537 | 0.2424 | 0.1896 | 0.2152 | 0.2424 | 0.2787 | 0.2068 | 0.2424 | 0.2424 | 0.2424 |
| 1.7426 | 3.0 | 87 | 1.6971 | 0.2272 | 0.3333 | 0.2622 | 0.1959 | 0.3333 | 0.2233 | 0.2846 | 0.3333 | 0.3333 | 0.3333 |
| 1.1847 | 4.0 | 116 | 1.5082 | 0.3360 | 0.4242 | 0.3911 | 0.3925 | 0.4242 | 0.4281 | 0.3553 | 0.4242 | 0.4242 | 0.4242 |
| 1.3906 | 5.0 | 145 | 1.4063 | 0.3152 | 0.4621 | 0.3815 | 0.2727 | 0.4621 | 0.3279 | 0.3785 | 0.4621 | 0.4621 | 0.4621 |
| 1.5575 | 6.0 | 174 | 1.3833 | 0.4414 | 0.4621 | 0.4526 | 0.4850 | 0.4621 | 0.4941 | 0.4402 | 0.4621 | 0.4621 | 0.4621 |
| 1.1063 | 7.0 | 203 | 1.2431 | 0.4750 | 0.5833 | 0.5453 | 0.5898 | 0.5833 | 0.6329 | 0.4890 | 0.5833 | 0.5833 | 0.5833 |
| 1.1503 | 8.0 | 232 | 1.3635 | 0.4036 | 0.4924 | 0.4586 | 0.4145 | 0.4924 | 0.4762 | 0.4436 | 0.4924 | 0.4924 | 0.4924 |
| 0.5124 | 9.0 | 261 | 1.1603 | 0.5463 | 0.6288 | 0.6136 | 0.5488 | 0.6288 | 0.6113 | 0.5551 | 0.6288 | 0.6288 | 0.6288 |
| 0.6648 | 10.0 | 290 | 1.4136 | 0.4184 | 0.5 | 0.4713 | 0.4713 | 0.5 | 0.5275 | 0.4413 | 0.5 | 0.5 | 0.5 |
| 0.2917 | 11.0 | 319 | 1.2004 | 0.5155 | 0.6061 | 0.5892 | 0.5180 | 0.6061 | 0.5882 | 0.5268 | 0.6061 | 0.6061 | 0.6061 |
| 0.4962 | 12.0 | 348 | 1.3730 | 0.4970 | 0.5682 | 0.5671 | 0.5094 | 0.5682 | 0.5909 | 0.5109 | 0.5682 | 0.5682 | 0.5682 |
| 0.5723 | 13.0 | 377 | 1.3377 | 0.5705 | 0.6136 | 0.6077 | 0.7050 | 0.6136 | 0.6879 | 0.5756 | 0.6136 | 0.6136 | 0.6136 |
| 0.4589 | 14.0 | 406 | 1.3717 | 0.5648 | 0.6136 | 0.6094 | 0.6239 | 0.6136 | 0.6458 | 0.5609 | 0.6136 | 0.6136 | 0.6136 |
| 0.2544 | 15.0 | 435 | 1.4129 | 0.5086 | 0.5985 | 0.5793 | 0.5140 | 0.5985 | 0.5772 | 0.5187 | 0.5985 | 0.5985 | 0.5985 |
| 0.3179 | 16.0 | 464 | 1.3589 | 0.5882 | 0.6439 | 0.6347 | 0.6912 | 0.6439 | 0.6603 | 0.5777 | 0.6439 | 0.6439 | 0.6439 |
| 0.1304 | 17.0 | 493 | 1.5604 | 0.5010 | 0.5758 | 0.5606 | 0.5123 | 0.5758 | 0.5669 | 0.5076 | 0.5758 | 0.5758 | 0.5758 |
| 0.0887 | 18.0 | 522 | 1.6231 | 0.5091 | 0.6061 | 0.5800 | 0.5344 | 0.6061 | 0.5917 | 0.5190 | 0.6061 | 0.6061 | 0.6061 |
| 0.0254 | 19.0 | 551 | 1.6095 | 0.5625 | 0.6136 | 0.6070 | 0.6642 | 0.6136 | 0.6353 | 0.5520 | 0.6136 | 0.6136 | 0.6136 |
| 0.0908 | 20.0 | 580 | 1.6941 | 0.5270 | 0.6136 | 0.5962 | 0.5331 | 0.6136 | 0.6004 | 0.5381 | 0.6136 | 0.6136 | 0.6136 |
| 0.0913 | 21.0 | 609 | 1.6917 | 0.5537 | 0.6136 | 0.6018 | 0.5909 | 0.6136 | 0.6169 | 0.5579 | 0.6136 | 0.6136 | 0.6136 |
| 0.015 | 22.0 | 638 | 1.8274 | 0.4866 | 0.5682 | 0.5512 | 0.4855 | 0.5682 | 0.5477 | 0.5003 | 0.5682 | 0.5682 | 0.5682 |
| 0.0156 | 23.0 | 667 | 1.7322 | 0.5772 | 0.6439 | 0.6233 | 0.6870 | 0.6439 | 0.6598 | 0.5802 | 0.6439 | 0.6439 | 0.6439 |
| 0.0275 | 24.0 | 696 | 1.6262 | 0.5293 | 0.6212 | 0.6006 | 0.5274 | 0.6212 | 0.5913 | 0.5422 | 0.6212 | 0.6212 | 0.6212 |
| 0.0034 | 25.0 | 725 | 1.7278 | 0.5680 | 0.6591 | 0.6409 | 0.5674 | 0.6591 | 0.6333 | 0.5786 | 0.6591 | 0.6591 | 0.6591 |
| 0.0021 | 26.0 | 754 | 1.7111 | 0.5542 | 0.6439 | 0.6250 | 0.5506 | 0.6439 | 0.6148 | 0.5657 | 0.6439 | 0.6439 | 0.6439 |
| 0.0021 | 27.0 | 783 | 1.7412 | 0.5556 | 0.6439 | 0.6257 | 0.5507 | 0.6439 | 0.6163 | 0.5684 | 0.6439 | 0.6439 | 0.6439 |
| 0.0079 | 28.0 | 812 | 1.8651 | 0.5506 | 0.6364 | 0.6176 | 0.5427 | 0.6364 | 0.6078 | 0.5670 | 0.6364 | 0.6364 | 0.6364 |
| 0.0018 | 29.0 | 841 | 1.8016 | 0.5508 | 0.6364 | 0.6184 | 0.5425 | 0.6364 | 0.6074 | 0.5654 | 0.6364 | 0.6364 | 0.6364 |
| 0.0068 | 30.0 | 870 | 1.7936 | 0.5510 | 0.6364 | 0.6182 | 0.5436 | 0.6364 | 0.6073 | 0.5650 | 0.6364 | 0.6364 | 0.6364 |
### Framework versions
- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0 |