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---
license: mit
base_model: microsoft/table-transformer-detection
tags:
- generated_from_trainer
model-index:
- name: margin-element-detector-fm-resilient-puddle-10
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. -->
# margin-element-detector-fm-resilient-puddle-10
This model is a fine-tuned version of [microsoft/table-transformer-detection](https://huggingface.co/microsoft/table-transformer-detection) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4052
- Loss Ce: 0.0393
- Loss Bbox: 0.0119
- Cardinality Error: 1.0210
- Giou: 84.6670
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Loss Ce | Loss Bbox | Cardinality Error | Giou |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:---------:|:-----------------:|:-------:|
| 1.8005 | 0.5 | 1250 | 1.7181 | 0.3317 | 0.0619 | 1.8440 | 46.1650 |
| 1.6365 | 1.0 | 2500 | 1.5861 | 0.3064 | 0.0540 | 2.0670 | 49.5198 |
| 1.4739 | 1.5 | 3750 | 1.4081 | 0.2414 | 0.0487 | 1.2300 | 53.8370 |
| 1.3831 | 2.0 | 5000 | 1.2797 | 0.1926 | 0.0424 | 1.3180 | 56.2369 |
| 1.2362 | 2.5 | 6250 | 1.2517 | 0.1801 | 0.0406 | 1.3390 | 56.5658 |
| 1.2328 | 3.0 | 7500 | 1.2189 | 0.1650 | 0.0387 | 1.2300 | 56.9758 |
| 1.1675 | 3.5 | 8750 | 1.0386 | 0.1388 | 0.0317 | 1.1000 | 62.9430 |
| 1.1411 | 4.0 | 10000 | 1.0574 | 0.1392 | 0.0347 | 1.0590 | 62.7719 |
| 1.0822 | 4.5 | 11250 | 1.0113 | 0.1187 | 0.0337 | 1.0750 | 63.8054 |
| 1.0703 | 5.0 | 12500 | 0.9718 | 0.1181 | 0.0301 | 1.0770 | 64.8419 |
| 1.0278 | 5.5 | 13750 | 0.9538 | 0.1284 | 0.0276 | 1.1210 | 65.6340 |
| 1.044 | 6.0 | 15000 | 0.9157 | 0.1087 | 0.0294 | 1.0430 | 67.0038 |
| 0.9623 | 6.5 | 16250 | 0.9210 | 0.1135 | 0.0291 | 1.0630 | 66.9005 |
| 0.9883 | 7.0 | 17500 | 0.9465 | 0.1058 | 0.0311 | 1.0280 | 65.7425 |
| 0.953 | 7.5 | 18750 | 0.9267 | 0.0954 | 0.0292 | 1.0160 | 65.7261 |
| 0.9673 | 8.0 | 20000 | 0.8716 | 0.0904 | 0.0259 | 1.0230 | 67.4044 |
| 0.8954 | 8.5 | 21250 | 0.8415 | 0.0812 | 0.0256 | 1.0260 | 68.3924 |
| 0.9177 | 9.0 | 22500 | 0.8036 | 0.0819 | 0.0237 | 1.0170 | 69.8347 |
| 0.8572 | 9.5 | 23750 | 0.8165 | 0.0782 | 0.0234 | 1.0130 | 68.9332 |
| 0.8408 | 10.0 | 25000 | 0.8299 | 0.0767 | 0.0235 | 1.0390 | 68.2173 |
| 0.8281 | 10.5 | 26250 | 0.7925 | 0.0824 | 0.0229 | 1.0150 | 70.2080 |
| 0.8488 | 11.0 | 27500 | 0.8325 | 0.0718 | 0.0260 | 0.9950 | 68.4594 |
| 0.7916 | 11.5 | 28750 | 0.8020 | 0.0785 | 0.0231 | 1.0410 | 69.5891 |
| 0.8569 | 12.0 | 30000 | 0.7565 | 0.0681 | 0.0223 | 1.0180 | 71.1528 |
| 0.8023 | 12.5 | 31250 | 0.7649 | 0.0687 | 0.0217 | 1.0190 | 70.6185 |
| 0.776 | 13.0 | 32500 | 0.7613 | 0.0688 | 0.0237 | 0.9970 | 71.3041 |
| 0.7715 | 13.5 | 33750 | 0.7440 | 0.0689 | 0.0215 | 0.9850 | 71.6202 |
| 0.7823 | 14.0 | 35000 | 0.7766 | 0.0717 | 0.0220 | 1.0280 | 70.2445 |
| 0.7579 | 14.5 | 36250 | 0.7339 | 0.0613 | 0.0205 | 1.0510 | 71.4997 |
| 0.7693 | 15.0 | 37500 | 0.7738 | 0.0661 | 0.0225 | 1.0220 | 70.2403 |
| 0.713 | 15.5 | 38750 | 0.6801 | 0.0614 | 0.0190 | 1.0430 | 73.8128 |
| 0.6734 | 16.0 | 40000 | 0.7041 | 0.0623 | 0.0213 | 1.0100 | 73.2345 |
| 0.7289 | 16.5 | 41250 | 0.6959 | 0.0607 | 0.0209 | 1.0060 | 73.4663 |
| 0.7205 | 17.0 | 42500 | 0.7272 | 0.0704 | 0.0215 | 1.0110 | 72.5326 |
| 0.6855 | 17.5 | 43750 | 0.6586 | 0.0624 | 0.0195 | 1.0330 | 75.0753 |
| 0.6523 | 18.0 | 45000 | 0.6495 | 0.0557 | 0.0192 | 1.0380 | 75.1177 |
| 0.6519 | 18.5 | 46250 | 0.6763 | 0.0589 | 0.0198 | 1.0060 | 74.0859 |
| 0.6568 | 19.0 | 47500 | 0.6548 | 0.0758 | 0.0181 | 1.0200 | 75.5647 |
| 0.6254 | 19.5 | 48750 | 0.6494 | 0.0584 | 0.0193 | 1.0320 | 75.2703 |
| 0.6487 | 20.0 | 50000 | 0.6183 | 0.0624 | 0.0183 | 1.0570 | 76.7859 |
| 0.6287 | 20.5 | 51250 | 0.6432 | 0.0565 | 0.0193 | 1.0010 | 75.4949 |
| 0.6163 | 21.0 | 52500 | 0.6062 | 0.0485 | 0.0162 | 1.0110 | 76.1785 |
| 0.6029 | 21.5 | 53750 | 0.6158 | 0.0504 | 0.0174 | 1.0200 | 76.0916 |
| 0.622 | 22.0 | 55000 | 0.6186 | 0.0546 | 0.0180 | 0.9950 | 76.3034 |
| 0.597 | 22.5 | 56250 | 0.6172 | 0.0513 | 0.0180 | 1.0120 | 76.2164 |
| 0.5684 | 23.0 | 57500 | 0.5967 | 0.0527 | 0.0175 | 1.0250 | 77.1797 |
| 0.5899 | 23.5 | 58750 | 0.6035 | 0.0538 | 0.0178 | 1.0250 | 76.9589 |
| 0.5592 | 24.0 | 60000 | 0.6320 | 0.0548 | 0.0179 | 1.0180 | 75.6223 |
| 0.5994 | 24.5 | 61250 | 0.5444 | 0.0529 | 0.0159 | 1.0210 | 79.3936 |
| 0.5547 | 25.0 | 62500 | 0.5969 | 0.0527 | 0.0174 | 1.0320 | 77.1495 |
| 0.5135 | 25.5 | 63750 | 0.5651 | 0.0524 | 0.0163 | 1.0310 | 78.4524 |
| 0.5504 | 26.0 | 65000 | 0.5823 | 0.0451 | 0.0172 | 1.0150 | 77.4492 |
| 0.5342 | 26.5 | 66250 | 0.5905 | 0.0489 | 0.0169 | 1.0090 | 77.1484 |
| 0.5166 | 27.0 | 67500 | 0.5651 | 0.0488 | 0.0157 | 1.0010 | 78.1068 |
| 0.5311 | 27.5 | 68750 | 0.5585 | 0.0532 | 0.0162 | 1.0280 | 78.7836 |
| 0.5178 | 28.0 | 70000 | 0.5315 | 0.0451 | 0.0152 | 1.0190 | 79.4811 |
| 0.4967 | 28.5 | 71250 | 0.5399 | 0.0518 | 0.0151 | 1.0210 | 79.3648 |
| 0.5137 | 29.0 | 72500 | 0.5199 | 0.0461 | 0.0143 | 1.0310 | 79.8946 |
| 0.4903 | 29.5 | 73750 | 0.4885 | 0.0470 | 0.0144 | 1.0100 | 81.5240 |
| 0.4739 | 30.0 | 75000 | 0.4985 | 0.0447 | 0.0134 | 1.0150 | 80.6692 |
| 0.4455 | 30.5 | 76250 | 0.4999 | 0.0461 | 0.0140 | 1.0290 | 80.8051 |
| 0.4476 | 31.0 | 77500 | 0.4961 | 0.0466 | 0.0140 | 1.0090 | 81.0313 |
| 0.4581 | 31.5 | 78750 | 0.4980 | 0.0406 | 0.0141 | 1.0310 | 80.6620 |
| 0.4413 | 32.0 | 80000 | 0.5194 | 0.0431 | 0.0144 | 1.0300 | 79.7935 |
| 0.4332 | 32.5 | 81250 | 0.4861 | 0.0423 | 0.0139 | 1.0270 | 81.2911 |
| 0.444 | 33.0 | 82500 | 0.4515 | 0.0408 | 0.0127 | 1.0290 | 82.6487 |
| 0.4323 | 33.5 | 83750 | 0.4629 | 0.0434 | 0.0134 | 1.0300 | 82.3851 |
| 0.4299 | 34.0 | 85000 | 0.4602 | 0.0403 | 0.0129 | 1.0220 | 82.2341 |
| 0.403 | 34.5 | 86250 | 0.4693 | 0.0440 | 0.0133 | 1.0350 | 82.0647 |
| 0.4001 | 35.0 | 87500 | 0.4582 | 0.0397 | 0.0132 | 1.0210 | 82.3646 |
| 0.3987 | 35.5 | 88750 | 0.4354 | 0.0405 | 0.0125 | 1.0220 | 83.3753 |
| 0.3814 | 36.0 | 90000 | 0.4327 | 0.0397 | 0.0129 | 1.0290 | 83.5913 |
| 0.3694 | 36.5 | 91250 | 0.4285 | 0.0395 | 0.0128 | 1.0370 | 83.7543 |
| 0.3791 | 37.0 | 92500 | 0.4262 | 0.0382 | 0.0123 | 1.0200 | 83.6733 |
| 0.3646 | 37.5 | 93750 | 0.4133 | 0.0406 | 0.0123 | 1.0460 | 84.4284 |
| 0.3756 | 38.0 | 95000 | 0.4211 | 0.0397 | 0.0121 | 1.0080 | 83.9594 |
| 0.3566 | 38.5 | 96250 | 0.4125 | 0.0382 | 0.0120 | 1.0190 | 84.2887 |
| 0.3601 | 39.0 | 97500 | 0.4082 | 0.0395 | 0.0119 | 1.0320 | 84.5329 |
| 0.3483 | 39.5 | 98750 | 0.4064 | 0.0395 | 0.0119 | 1.0230 | 84.6185 |
| 0.3485 | 40.0 | 100000 | 0.4052 | 0.0393 | 0.0119 | 1.0210 | 84.6670 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
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