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detr_finetuned_cppe5

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conditional_detr
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metadata 
library_name: transformers
license: apache-2.0
base_model: microsoft/conditional-detr-resnet-50
tags:
  - generated_from_trainer
model-index:
  - name: detr_finetuned_cppe5
    results: []

detr_finetuned_cppe5

This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8041
  • Map: 0.4041
  • Map 50: 0.8246
  • Map 75: 0.3402
  • Map Small: 0.3079
  • Map Medium: 0.3562
  • Map Large: 0.6364
  • Mar 1: 0.1839
  • Mar 10: 0.4794
  • Mar 100: 0.5657
  • Mar Small: 0.4329
  • Mar Medium: 0.5174
  • Mar Large: 0.7856
  • Map Hardhat: 0.4075
  • Mar 100 Hardhat: 0.5473
  • Map No-hardhat: 0.4007
  • Mar 100 No-hardhat: 0.5842

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: 8
  • eval_batch_size: 8
  • 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: cosine
  • num_epochs: 30

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 Map Hardhat Mar 100 Hardhat Map No-hardhat Mar 100 No-hardhat
No log 1.0 125 1.1847 0.0822 0.1954 0.0553 0.0796 0.0937 0.2324 0.1312 0.3509 0.4354 0.2086 0.3775 0.6788 0.1426 0.5655 0.0218 0.3053
No log 2.0 250 1.0931 0.1205 0.2648 0.0965 0.1277 0.1601 0.1647 0.1587 0.3817 0.4625 0.2886 0.435 0.6636 0.1868 0.5618 0.0542 0.3632
No log 3.0 375 1.1882 0.1449 0.3805 0.0834 0.0775 0.1623 0.3615 0.1042 0.3262 0.4281 0.3086 0.347 0.5962 0.2321 0.5036 0.0577 0.3526
1.472 4.0 500 1.0418 0.2419 0.6022 0.1416 0.1374 0.3265 0.3723 0.1444 0.4138 0.4827 0.2986 0.5035 0.6114 0.2844 0.5127 0.1994 0.4526
1.472 5.0 625 1.0232 0.2349 0.5815 0.1564 0.1736 0.3006 0.3826 0.1395 0.3992 0.4974 0.2686 0.5149 0.6689 0.2891 0.5527 0.1807 0.4421
1.472 6.0 750 0.9985 0.293 0.6561 0.2108 0.2129 0.3184 0.426 0.1604 0.4373 0.5125 0.3129 0.5225 0.6879 0.3452 0.5618 0.2407 0.4632
1.472 7.0 875 0.9616 0.3145 0.7258 0.2615 0.2491 0.3435 0.4999 0.1381 0.4634 0.5455 0.3171 0.5568 0.7386 0.3164 0.5436 0.3126 0.5474
0.9939 8.0 1000 0.9688 0.3194 0.7461 0.1786 0.1922 0.3171 0.576 0.16 0.4325 0.5252 0.2943 0.5194 0.7462 0.2982 0.4873 0.3405 0.5632
0.9939 9.0 1125 0.9211 0.3572 0.7888 0.2944 0.2046 0.3196 0.5454 0.1482 0.4632 0.5346 0.2571 0.5625 0.7303 0.3545 0.5218 0.3599 0.5474
0.9939 10.0 1250 0.9664 0.3463 0.7569 0.2541 0.2503 0.3473 0.4924 0.1611 0.4344 0.5116 0.31 0.499 0.7258 0.3423 0.5127 0.3504 0.5105
0.9939 11.0 1375 0.9463 0.3261 0.8263 0.2012 0.2582 0.2676 0.5931 0.1643 0.4153 0.5134 0.32 0.4437 0.7629 0.3386 0.5164 0.3136 0.5105
0.8775 12.0 1500 0.9153 0.3571 0.7972 0.2882 0.2397 0.3273 0.5803 0.1587 0.4377 0.5556 0.3571 0.566 0.7189 0.3347 0.5164 0.3795 0.5947
0.8775 13.0 1625 0.9063 0.3512 0.8299 0.2471 0.2342 0.3119 0.6117 0.1695 0.4222 0.5016 0.2786 0.4672 0.7439 0.3422 0.4927 0.3602 0.5105
0.8775 14.0 1750 0.9384 0.3351 0.7633 0.2393 0.1938 0.3064 0.5551 0.1723 0.4257 0.5105 0.3371 0.4718 0.7076 0.3496 0.5 0.3206 0.5211
0.8775 15.0 1875 0.8734 0.3836 0.8279 0.3055 0.2541 0.348 0.614 0.1748 0.4373 0.531 0.3729 0.4941 0.7386 0.3671 0.5145 0.4002 0.5474
0.7888 16.0 2000 0.8470 0.3763 0.8437 0.2603 0.2854 0.3289 0.5821 0.1822 0.4556 0.5455 0.4314 0.4861 0.7568 0.3894 0.5436 0.3633 0.5474
0.7888 17.0 2125 0.8579 0.3708 0.8189 0.2792 0.2701 0.2976 0.6115 0.185 0.443 0.5206 0.3957 0.4633 0.7326 0.3986 0.5255 0.343 0.5158
0.7888 18.0 2250 0.8404 0.3714 0.7962 0.2522 0.2531 0.3327 0.6139 0.1778 0.4587 0.5433 0.3729 0.5084 0.7455 0.3709 0.5182 0.3719 0.5684
0.7888 19.0 2375 0.8268 0.3997 0.8285 0.3108 0.2915 0.3526 0.5942 0.1829 0.4882 0.5481 0.4157 0.4986 0.7644 0.4014 0.5436 0.3979 0.5526
0.7048 20.0 2500 0.8091 0.4209 0.8122 0.4316 0.2668 0.377 0.6569 0.1964 0.4669 0.5568 0.4 0.5206 0.7462 0.4154 0.54 0.4265 0.5737
0.7048 21.0 2625 0.8206 0.416 0.8227 0.303 0.3221 0.3747 0.6208 0.1839 0.4811 0.5401 0.3729 0.4992 0.747 0.4198 0.5382 0.4121 0.5421
0.7048 22.0 2750 0.8108 0.4266 0.8502 0.4021 0.3038 0.3847 0.6317 0.1965 0.4688 0.5534 0.4257 0.5125 0.7515 0.4264 0.5436 0.4269 0.5632
0.7048 23.0 2875 0.8239 0.4103 0.8158 0.3492 0.2874 0.3626 0.6316 0.1919 0.4572 0.5533 0.4114 0.5152 0.7462 0.417 0.5382 0.4036 0.5684
0.6439 24.0 3000 0.8092 0.4077 0.825 0.3504 0.3205 0.3525 0.6074 0.1893 0.4883 0.5641 0.4357 0.5228 0.7652 0.4129 0.5545 0.4026 0.5737
0.6439 25.0 3125 0.8076 0.4104 0.8432 0.3547 0.316 0.3559 0.6302 0.1893 0.4689 0.5535 0.4429 0.5027 0.7515 0.4187 0.5491 0.4021 0.5579
0.6439 26.0 3250 0.7988 0.4133 0.837 0.3469 0.3285 0.3631 0.6222 0.2035 0.4849 0.573 0.4643 0.5166 0.7902 0.4219 0.5618 0.4048 0.5842
0.6439 27.0 3375 0.8015 0.4082 0.832 0.3262 0.3104 0.3619 0.62 0.1699 0.4785 0.5693 0.4429 0.5174 0.7902 0.4165 0.5491 0.3998 0.5895
0.6079 28.0 3500 0.8043 0.4064 0.824 0.3412 0.3052 0.3588 0.6301 0.1857 0.4785 0.5631 0.4329 0.5111 0.7856 0.4145 0.5473 0.3982 0.5789
0.6079 29.0 3625 0.8043 0.403 0.8246 0.3378 0.3082 0.3533 0.6358 0.1839 0.4794 0.5667 0.44 0.5174 0.7856 0.4076 0.5491 0.3984 0.5842
0.6079 30.0 3750 0.8041 0.4041 0.8246 0.3402 0.3079 0.3562 0.6364 0.1839 0.4794 0.5657 0.4329 0.5174 0.7856 0.4075 0.5473 0.4007 0.5842

Framework versions

  • Transformers 4.51.3
  • Pytorch 2.7.0+cu126
  • Datasets 3.6.0
  • Tokenizers 0.21.1