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---
library_name: transformers
license: apache-2.0
base_model: facebook/detr-resnet-50-dc5
tags:
- generated_from_trainer
model-index:
- name: facebook/detr-resnet-50-dc5
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. -->
# facebook/detr-resnet-50-dc5
This model is a fine-tuned version of [facebook/detr-resnet-50-dc5](https://huggingface.co/facebook/detr-resnet-50-dc5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5836
- Map: 0.5257
- Map 50: 0.6508
- Map 75: 0.6241
- Map Small: 0.0
- Map Medium: 0.4752
- Map Large: 0.7513
- Mar 1: 0.1853
- Mar 10: 0.6
- Mar 100: 0.7147
- Mar Small: 0.0
- Mar Medium: 0.6684
- Mar Large: 0.8923
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|
| 4.1002 | 0.7692 | 10 | 4.1741 | 0.0003 | 0.001 | 0.0003 | 0.0 | 0.0062 | 0.0002 | 0.0 | 0.0 | 0.0441 | 0.0 | 0.0474 | 0.0462 |
| 1.772 | 1.5385 | 20 | 1.4577 | 0.0298 | 0.05 | 0.0286 | 0.0 | 0.0185 | 0.0656 | 0.0294 | 0.1206 | 0.4882 | 0.0 | 0.3421 | 0.7769 |
| 1.5665 | 2.3077 | 30 | 1.3869 | 0.0339 | 0.0549 | 0.0351 | 0.0 | 0.0407 | 0.0516 | 0.0029 | 0.0824 | 0.6059 | 0.0 | 0.5158 | 0.8308 |
| 2.0258 | 3.0769 | 40 | 1.2246 | 0.0561 | 0.0797 | 0.0593 | 0.0 | 0.0398 | 0.1166 | 0.0265 | 0.1206 | 0.6441 | 0.0 | 0.5789 | 0.8385 |
| 1.5082 | 3.8462 | 50 | 1.1988 | 0.0477 | 0.0869 | 0.0542 | 0.0 | 0.0927 | 0.063 | 0.0235 | 0.0853 | 0.6471 | 0.0 | 0.6316 | 0.7692 |
| 1.3716 | 4.6154 | 60 | 1.1917 | 0.0549 | 0.1014 | 0.0602 | 0.0 | 0.0902 | 0.0761 | 0.0588 | 0.1618 | 0.5971 | 0.0 | 0.5421 | 0.7692 |
| 1.2398 | 5.3846 | 70 | 1.0554 | 0.1329 | 0.1674 | 0.1485 | 0.0 | 0.1462 | 0.1957 | 0.0765 | 0.1882 | 0.7294 | 0.0 | 0.7474 | 0.8154 |
| 1.401 | 6.1538 | 80 | 0.9179 | 0.1176 | 0.1821 | 0.1315 | 0.0 | 0.0835 | 0.2295 | 0.0529 | 0.1794 | 0.7294 | 0.0 | 0.7211 | 0.8538 |
| 2.0328 | 6.9231 | 90 | 0.9198 | 0.1361 | 0.2109 | 0.1554 | 0.0 | 0.0937 | 0.2424 | 0.0559 | 0.2088 | 0.6882 | 0.0 | 0.6368 | 0.8692 |
| 1.6358 | 7.6923 | 100 | 0.9298 | 0.2252 | 0.2898 | 0.2523 | 0.0 | 0.2279 | 0.3487 | 0.1059 | 0.3176 | 0.6882 | 0.0 | 0.6263 | 0.8846 |
| 0.8849 | 8.4615 | 110 | 0.8894 | 0.1893 | 0.2435 | 0.2248 | 0.0 | 0.1438 | 0.3337 | 0.0971 | 0.2265 | 0.7265 | 0.0 | 0.7263 | 0.8385 |
| 1.1906 | 9.2308 | 120 | 0.8505 | 0.2105 | 0.2704 | 0.2598 | 0.0 | 0.1879 | 0.3317 | 0.1324 | 0.2706 | 0.6853 | 0.0 | 0.6474 | 0.8462 |
| 1.0404 | 10.0 | 130 | 0.7320 | 0.2508 | 0.2998 | 0.29 | 0.0 | 0.2031 | 0.4149 | 0.1588 | 0.2971 | 0.7471 | 0.0 | 0.7421 | 0.8692 |
| 1.1534 | 10.7692 | 140 | 0.7996 | 0.2832 | 0.374 | 0.3479 | 0.0 | 0.2502 | 0.411 | 0.1676 | 0.3647 | 0.6647 | 0.0 | 0.6263 | 0.8231 |
| 1.1725 | 11.5385 | 150 | 0.7990 | 0.3115 | 0.4464 | 0.3745 | 0.0 | 0.2972 | 0.4147 | 0.1294 | 0.3735 | 0.6588 | 0.0 | 0.6158 | 0.8231 |
| 0.891 | 12.3077 | 160 | 0.9007 | 0.2856 | 0.3519 | 0.3449 | 0.0 | 0.2607 | 0.3788 | 0.1029 | 0.3529 | 0.6735 | 0.0 | 0.6263 | 0.8462 |
| 1.1 | 13.0769 | 170 | 0.7376 | 0.2642 | 0.3608 | 0.3377 | 0.0 | 0.2281 | 0.4018 | 0.1176 | 0.3676 | 0.7176 | 0.0 | 0.7 | 0.8538 |
| 1.2631 | 13.8462 | 180 | 0.7162 | 0.306 | 0.4363 | 0.3899 | 0.0 | 0.2997 | 0.3933 | 0.1412 | 0.45 | 0.7059 | 0.0 | 0.7053 | 0.8154 |
| 1.0496 | 14.6154 | 190 | 0.7276 | 0.2811 | 0.3866 | 0.3483 | 0.0 | 0.3061 | 0.3685 | 0.1471 | 0.3882 | 0.7235 | 0.0 | 0.7316 | 0.8231 |
| 0.8883 | 15.3846 | 200 | 0.6855 | 0.3373 | 0.4578 | 0.4385 | 0.0 | 0.3441 | 0.4654 | 0.15 | 0.4824 | 0.7412 | 0.0 | 0.7579 | 0.8308 |
| 0.8471 | 16.1538 | 210 | 0.6733 | 0.4351 | 0.5932 | 0.5367 | 0.0 | 0.3702 | 0.6215 | 0.15 | 0.5412 | 0.7206 | 0.0 | 0.7158 | 0.8385 |
| 0.9084 | 16.9231 | 220 | 0.6526 | 0.4279 | 0.5632 | 0.4848 | 0.0 | 0.4011 | 0.572 | 0.1824 | 0.5647 | 0.7294 | 0.0 | 0.7105 | 0.8692 |
| 0.8872 | 17.6923 | 230 | 0.6218 | 0.4376 | 0.5753 | 0.5274 | 0.0 | 0.3879 | 0.6215 | 0.1559 | 0.5853 | 0.7382 | 0.0 | 0.7263 | 0.8692 |
| 0.9739 | 18.4615 | 240 | 0.6590 | 0.4494 | 0.6293 | 0.505 | 0.0 | 0.3889 | 0.65 | 0.1471 | 0.5853 | 0.7029 | 0.0 | 0.6895 | 0.8308 |
| 0.7596 | 19.2308 | 250 | 0.6367 | 0.4625 | 0.6229 | 0.5322 | 0.0 | 0.4106 | 0.6581 | 0.1529 | 0.5853 | 0.7118 | 0.0 | 0.7053 | 0.8308 |
| 0.7124 | 20.0 | 260 | 0.6601 | 0.4619 | 0.6411 | 0.5327 | 0.0 | 0.39 | 0.6852 | 0.1559 | 0.5765 | 0.6794 | 0.0 | 0.6421 | 0.8385 |
| 0.8369 | 20.7692 | 270 | 0.6363 | 0.4736 | 0.64 | 0.5738 | 0.0 | 0.3993 | 0.737 | 0.1559 | 0.5853 | 0.6853 | 0.0 | 0.6474 | 0.8462 |
| 0.8608 | 21.5385 | 280 | 0.6304 | 0.496 | 0.6406 | 0.5583 | 0.0 | 0.4484 | 0.6973 | 0.1588 | 0.5912 | 0.7 | 0.0 | 0.6579 | 0.8692 |
| 0.6174 | 22.3077 | 290 | 0.6825 | 0.4808 | 0.6714 | 0.5569 | 0.0 | 0.4264 | 0.6738 | 0.1529 | 0.5765 | 0.6735 | 0.0 | 0.6158 | 0.8615 |
| 0.5903 | 23.0769 | 300 | 0.6037 | 0.5187 | 0.6804 | 0.6126 | 0.0 | 0.4604 | 0.709 | 0.1824 | 0.6118 | 0.7206 | 0.0 | 0.6842 | 0.8846 |
| 0.6325 | 23.8462 | 310 | 0.6373 | 0.529 | 0.6819 | 0.6246 | 0.0 | 0.4489 | 0.7601 | 0.1765 | 0.5941 | 0.7088 | 0.0 | 0.6579 | 0.8923 |
| 0.8569 | 24.6154 | 320 | 0.6131 | 0.5382 | 0.6684 | 0.6357 | 0.0 | 0.4862 | 0.7382 | 0.1794 | 0.6147 | 0.7294 | 0.0 | 0.7 | 0.8846 |
| 0.7056 | 25.3846 | 330 | 0.5700 | 0.5244 | 0.6545 | 0.6089 | 0.0 | 0.4891 | 0.6871 | 0.1824 | 0.6176 | 0.75 | 0.0 | 0.7421 | 0.8769 |
| 0.5988 | 26.1538 | 340 | 0.5738 | 0.5437 | 0.7119 | 0.651 | 0.0 | 0.5362 | 0.6823 | 0.1853 | 0.6206 | 0.7529 | 0.0 | 0.7579 | 0.8615 |
| 0.5209 | 26.9231 | 350 | 0.6136 | 0.5153 | 0.6944 | 0.6047 | 0.0 | 0.4772 | 0.7054 | 0.1824 | 0.5882 | 0.7059 | 0.0 | 0.6789 | 0.8538 |
| 0.6547 | 27.6923 | 360 | 0.6338 | 0.5166 | 0.6645 | 0.6224 | 0.0 | 0.4842 | 0.7072 | 0.1882 | 0.5971 | 0.7088 | 0.0 | 0.6842 | 0.8538 |
| 0.6324 | 28.4615 | 370 | 0.6083 | 0.5143 | 0.6543 | 0.6279 | 0.0 | 0.4683 | 0.729 | 0.1853 | 0.6 | 0.7118 | 0.0 | 0.6789 | 0.8692 |
| 0.6323 | 29.2308 | 380 | 0.5748 | 0.529 | 0.6552 | 0.637 | 0.0 | 0.48 | 0.7529 | 0.1853 | 0.6088 | 0.7206 | 0.0 | 0.6842 | 0.8846 |
| 0.4509 | 30.0 | 390 | 0.5758 | 0.5311 | 0.652 | 0.6325 | 0.0 | 0.4923 | 0.7454 | 0.1882 | 0.6206 | 0.7324 | 0.0 | 0.7053 | 0.8846 |
| 0.8259 | 30.7692 | 400 | 0.5836 | 0.5257 | 0.6508 | 0.6241 | 0.0 | 0.4752 | 0.7513 | 0.1853 | 0.6 | 0.7147 | 0.0 | 0.6684 | 0.8923 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.0
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