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---
library_name: transformers
license: apache-2.0
base_model: PekingU/rtdetr_r50vd
tags:
- generated_from_trainer
model-index:
- name: suas-2025-rtdetr-finetuned-b16-lr1e-5
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. -->
# suas-2025-rtdetr-finetuned-b16-lr1e-5
This model is a fine-tuned version of [PekingU/rtdetr_r50vd](https://huggingface.co/PekingU/rtdetr_r50vd) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6523
- Map: 0.8465
- Map 50: 0.9229
- Map 75: 0.9193
- Map Small: 0.7682
- Map Medium: 0.8561
- Map Large: 0.9144
- Mar 1: 0.7945
- Mar 10: 0.9245
- Mar 100: 0.9271
- Mar Small: 0.8316
- Mar Medium: 0.9357
- Mar Large: 0.9779
- Map Baseball-bat: 0.8128
- Mar 100 Baseball-bat: 0.892
- Map Basketball: 0.8105
- Mar 100 Basketball: 0.8993
- Map Car: -1.0
- Mar 100 Car: -1.0
- Map Football: 0.7611
- Mar 100 Football: 0.8113
- Map Human: 0.9382
- Mar 100 Human: 0.9641
- Map Luggage: 0.8579
- Mar 100 Luggage: 0.9191
- Map Mattress: 0.9384
- Mar 100 Mattress: 0.977
- Map Motorcycle: 0.9309
- Mar 100 Motorcycle: 0.9773
- Map Skis: 0.7044
- Mar 100 Skis: 0.995
- Map Snowboard: 0.9834
- Mar 100 Snowboard: 0.9932
- Map Soccer-ball: 0.8245
- Mar 100 Soccer-ball: 0.8733
- Map Stop-sign: 0.9671
- Mar 100 Stop-sign: 0.9925
- Map Tennis-racket: 0.7064
- Mar 100 Tennis-racket: 0.8539
- Map Umbrella: 0.8285
- Mar 100 Umbrella: 0.9803
- Map Volleyball: 0.787
- Mar 100 Volleyball: 0.8517
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 1337
- 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
- num_epochs: 10.0
- 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 | Map Baseball-bat | Mar 100 Baseball-bat | Map Basketball | Mar 100 Basketball | Map Car | Mar 100 Car | Map Football | Mar 100 Football | Map Human | Mar 100 Human | Map Luggage | Mar 100 Luggage | Map Mattress | Mar 100 Mattress | Map Motorcycle | Mar 100 Motorcycle | Map Skis | Mar 100 Skis | Map Snowboard | Mar 100 Snowboard | Map Soccer-ball | Mar 100 Soccer-ball | Map Stop-sign | Mar 100 Stop-sign | Map Tennis-racket | Mar 100 Tennis-racket | Map Umbrella | Mar 100 Umbrella | Map Volleyball | Mar 100 Volleyball |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:----------------:|:--------------------:|:--------------:|:------------------:|:-------:|:-----------:|:------------:|:----------------:|:---------:|:-------------:|:-----------:|:---------------:|:------------:|:----------------:|:--------------:|:------------------:|:--------:|:------------:|:-------------:|:-----------------:|:---------------:|:-------------------:|:-------------:|:-----------------:|:-----------------:|:---------------------:|:------------:|:----------------:|:--------------:|:------------------:|
| 26.4817 | 1.0 | 438 | 7.1796 | 0.5246 | 0.6136 | 0.5998 | 0.4998 | 0.4541 | 0.685 | 0.6337 | 0.8316 | 0.8375 | 0.7077 | 0.8446 | 0.9453 | 0.3868 | 0.8129 | 0.1579 | 0.8832 | -1.0 | -1.0 | 0.5107 | 0.6099 | 0.8625 | 0.9246 | 0.6942 | 0.792 | 0.568 | 0.8388 | 0.7128 | 0.9384 | 0.085 | 0.9099 | 0.9532 | 0.9784 | 0.5658 | 0.7478 | 0.3822 | 0.9553 | 0.2726 | 0.7264 | 0.6033 | 0.9515 | 0.5895 | 0.6554 |
| 8.2849 | 2.0 | 876 | 5.1650 | 0.6623 | 0.7385 | 0.7311 | 0.6108 | 0.674 | 0.8009 | 0.6914 | 0.87 | 0.8726 | 0.7227 | 0.9081 | 0.9696 | 0.7515 | 0.8723 | 0.2886 | 0.8671 | -1.0 | -1.0 | 0.6431 | 0.7019 | 0.9057 | 0.9462 | 0.8014 | 0.8587 | 0.8949 | 0.9605 | 0.8986 | 0.9603 | 0.1247 | 0.9936 | 0.9665 | 0.9862 | 0.7817 | 0.8419 | 0.8546 | 0.9869 | 0.0862 | 0.7197 | 0.7593 | 0.9635 | 0.5151 | 0.5581 |
| 6.559 | 3.0 | 1314 | 4.4005 | 0.7504 | 0.8276 | 0.8215 | 0.7114 | 0.7272 | 0.8594 | 0.7435 | 0.8949 | 0.8966 | 0.7711 | 0.9093 | 0.975 | 0.7706 | 0.8428 | 0.6714 | 0.8801 | -1.0 | -1.0 | 0.6961 | 0.7498 | 0.9191 | 0.9553 | 0.8097 | 0.8663 | 0.9315 | 0.981 | 0.9122 | 0.9665 | 0.356 | 0.9926 | 0.9799 | 0.9927 | 0.8045 | 0.8594 | 0.9288 | 0.9846 | 0.2706 | 0.7788 | 0.7865 | 0.9736 | 0.6683 | 0.7284 |
| 6.1007 | 4.0 | 1752 | 4.3050 | 0.7733 | 0.8501 | 0.8445 | 0.672 | 0.8036 | 0.8698 | 0.75 | 0.8933 | 0.8959 | 0.7471 | 0.93 | 0.977 | 0.7927 | 0.8788 | 0.5734 | 0.7517 | -1.0 | -1.0 | 0.7111 | 0.7712 | 0.9262 | 0.959 | 0.8357 | 0.8953 | 0.9221 | 0.966 | 0.9326 | 0.9731 | 0.4091 | 0.9936 | 0.9788 | 0.9937 | 0.7975 | 0.8563 | 0.9535 | 0.9877 | 0.5879 | 0.8409 | 0.798 | 0.9753 | 0.6073 | 0.6998 |
| 5.7812 | 5.0 | 2190 | 3.9205 | 0.8281 | 0.91 | 0.9046 | 0.741 | 0.8373 | 0.9087 | 0.7819 | 0.9162 | 0.9196 | 0.8073 | 0.9294 | 0.9776 | 0.8105 | 0.8866 | 0.7005 | 0.8344 | -1.0 | -1.0 | 0.7656 | 0.8203 | 0.9326 | 0.9609 | 0.8291 | 0.9 | 0.9399 | 0.9791 | 0.9223 | 0.9695 | 0.6417 | 0.9906 | 0.977 | 0.9935 | 0.8194 | 0.8738 | 0.9422 | 0.9858 | 0.7258 | 0.8819 | 0.8433 | 0.9767 | 0.744 | 0.8208 |
| 5.4833 | 6.0 | 2628 | 3.7494 | 0.8402 | 0.9191 | 0.915 | 0.7567 | 0.861 | 0.9247 | 0.7947 | 0.9219 | 0.9246 | 0.8235 | 0.9378 | 0.9902 | 0.8153 | 0.8907 | 0.8096 | 0.9043 | -1.0 | -1.0 | 0.7586 | 0.8083 | 0.9378 | 0.9627 | 0.8322 | 0.9015 | 0.9545 | 0.989 | 0.9241 | 0.9725 | 0.7303 | 0.9926 | 0.9832 | 0.9947 | 0.8225 | 0.8707 | 0.9542 | 0.988 | 0.6731 | 0.871 | 0.8273 | 0.9776 | 0.7398 | 0.8213 |
| 5.3134 | 7.0 | 3066 | 3.7250 | 0.8437 | 0.9213 | 0.9165 | 0.7597 | 0.854 | 0.919 | 0.7917 | 0.9213 | 0.9241 | 0.8256 | 0.9419 | 0.9775 | 0.8142 | 0.8876 | 0.8066 | 0.9014 | -1.0 | -1.0 | 0.7341 | 0.7887 | 0.9359 | 0.9624 | 0.8417 | 0.9133 | 0.9412 | 0.9782 | 0.9292 | 0.9777 | 0.713 | 0.9931 | 0.9828 | 0.9934 | 0.828 | 0.8761 | 0.963 | 0.9922 | 0.7265 | 0.8554 | 0.8307 | 0.9791 | 0.7646 | 0.8392 |
| 5.2982 | 8.0 | 3504 | 3.7825 | 0.8419 | 0.9181 | 0.9133 | 0.7599 | 0.8549 | 0.9154 | 0.7909 | 0.9198 | 0.922 | 0.8277 | 0.9355 | 0.9768 | 0.8151 | 0.8931 | 0.8242 | 0.9066 | -1.0 | -1.0 | 0.7225 | 0.772 | 0.9375 | 0.9627 | 0.8383 | 0.9052 | 0.9338 | 0.9744 | 0.9288 | 0.9755 | 0.7353 | 0.996 | 0.9818 | 0.9925 | 0.8221 | 0.873 | 0.9675 | 0.9891 | 0.6801 | 0.8466 | 0.8273 | 0.9818 | 0.7726 | 0.8397 |
| 5.1855 | 9.0 | 3942 | 3.7947 | 0.8365 | 0.913 | 0.9085 | 0.7528 | 0.8439 | 0.9104 | 0.7884 | 0.9184 | 0.9219 | 0.8189 | 0.9344 | 0.979 | 0.8041 | 0.8864 | 0.8009 | 0.8955 | -1.0 | -1.0 | 0.7239 | 0.7755 | 0.9351 | 0.9622 | 0.8444 | 0.9119 | 0.9314 | 0.9771 | 0.9241 | 0.9735 | 0.7034 | 0.995 | 0.984 | 0.995 | 0.8168 | 0.8704 | 0.9639 | 0.9922 | 0.6888 | 0.8513 | 0.8203 | 0.9808 | 0.7704 | 0.8402 |
| 5.1499 | 10.0 | 4380 | 3.6523 | 0.8465 | 0.9229 | 0.9193 | 0.7682 | 0.8561 | 0.9144 | 0.7945 | 0.9245 | 0.9271 | 0.8316 | 0.9357 | 0.9779 | 0.8128 | 0.892 | 0.8105 | 0.8993 | -1.0 | -1.0 | 0.7611 | 0.8113 | 0.9382 | 0.9641 | 0.8579 | 0.9191 | 0.9384 | 0.977 | 0.9309 | 0.9773 | 0.7044 | 0.995 | 0.9834 | 0.9932 | 0.8245 | 0.8733 | 0.9671 | 0.9925 | 0.7064 | 0.8539 | 0.8285 | 0.9803 | 0.787 | 0.8517 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0
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