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---
library_name: transformers
license: apache-2.0
base_model: PekingU/rtdetr_r50vd
tags:
- object-detection
- vision
- generated_from_trainer
model-index:
- name: suas-2025-rtdetr-finetuned-b8-lr3e-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-b8-lr3e-5
This model is a fine-tuned version of [PekingU/rtdetr_r50vd](https://huggingface.co/PekingU/rtdetr_r50vd) on the mfly-auton/suas-2025-synthetic-data dataset.
It achieves the following results on the evaluation set:
- Loss: 6.4174
- Map: 0.6051
- Map 50: 0.7291
- Map 75: 0.6597
- Map Small: 0.5252
- Map Medium: 0.6131
- Map Large: 0.6723
- Mar 1: 0.6006
- Mar 10: 0.7415
- Mar 100: 0.7959
- Mar Small: 0.5831
- Mar Medium: 0.8299
- Mar Large: 0.948
- Map Baseball-bat: 0.5841
- Mar 100 Baseball-bat: 0.7219
- Map Basketball: 0.4357
- Mar 100 Basketball: 0.4955
- Map Car: -1.0
- Mar 100 Car: -1.0
- Map Football: 0.4363
- Mar 100 Football: 0.5014
- Map Human: 0.9189
- Mar 100 Human: 0.9512
- Map Luggage: 0.5995
- Mar 100 Luggage: 0.8773
- Map Mattress: 0.7881
- Mar 100 Mattress: 0.9906
- Map Motorcycle: 0.9485
- Mar 100 Motorcycle: 0.9752
- Map Skis: 0.9582
- Mar 100 Skis: 0.9782
- Map Snowboard: 0.0257
- Mar 100 Snowboard: 0.777
- Map Soccer-ball: 0.6432
- Mar 100 Soccer-ball: 0.7154
- Map Stop-sign: 0.5994
- Mar 100 Stop-sign: 0.9274
- Map Tennis-racket: 0.0019
- Mar 100 Tennis-racket: 0.5736
- Map Umbrella: 0.8665
- Mar 100 Umbrella: 0.9243
- Map Volleyball: 0.6647
- Mar 100 Volleyball: 0.7338
## 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: 3e-05
- train_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:----------------:|:--------------------:|:--------------:|:------------------:|:-------:|:-----------:|:------------:|:----------------:|:---------:|:-------------:|:-----------:|:---------------:|:------------:|:----------------:|:--------------:|:------------------:|:--------:|:------------:|:-------------:|:-----------------:|:---------------:|:-------------------:|:-------------:|:-----------------:|:-----------------:|:---------------------:|:------------:|:----------------:|:--------------:|:------------------:|
| 17.7551 | 1.0 | 875 | 5.2510 | 0.6942 | 0.7838 | 0.7782 | 0.7254 | 0.6405 | 0.7363 | 0.7413 | 0.8942 | 0.9051 | 0.8089 | 0.8996 | 0.9496 | 0.6964 | 0.8173 | 0.7915 | 0.8415 | -1.0 | -1.0 | 0.7303 | 0.8192 | 0.8746 | 0.9281 | 0.6526 | 0.9372 | 0.9142 | 0.9711 | 0.8867 | 0.9311 | 0.8376 | 0.9594 | 0.3429 | 0.9742 | 0.7454 | 0.8686 | 0.581 | 0.9542 | 0.3918 | 0.944 | 0.4713 | 0.8414 | 0.8027 | 0.8846 |
| 6.6319 | 2.0 | 1750 | 5.6671 | 0.6915 | 0.7796 | 0.7715 | 0.6499 | 0.6468 | 0.8104 | 0.7323 | 0.879 | 0.892 | 0.726 | 0.9168 | 0.9841 | 0.6895 | 0.7787 | 0.5032 | 0.6076 | -1.0 | -1.0 | 0.7195 | 0.7926 | 0.9186 | 0.9593 | 0.6184 | 0.9288 | 0.895 | 0.9984 | 0.901 | 0.9643 | 0.9222 | 0.9634 | 0.2897 | 0.9699 | 0.7717 | 0.8578 | 0.6219 | 0.9584 | 0.2013 | 0.8995 | 0.8724 | 0.9458 | 0.7562 | 0.864 |
| 6.3092 | 3.0 | 2625 | 5.3786 | 0.6861 | 0.7797 | 0.7703 | 0.6794 | 0.6505 | 0.8039 | 0.7008 | 0.8593 | 0.8895 | 0.738 | 0.9294 | 0.9851 | 0.7854 | 0.863 | 0.5392 | 0.5953 | -1.0 | -1.0 | 0.6451 | 0.7011 | 0.9165 | 0.9458 | 0.6485 | 0.9354 | 0.9011 | 0.9986 | 0.9194 | 0.9592 | 0.9354 | 0.9827 | 0.147 | 0.9815 | 0.8089 | 0.8686 | 0.7102 | 0.9732 | 0.0527 | 0.8694 | 0.8651 | 0.9418 | 0.7312 | 0.838 |
| 5.8652 | 4.0 | 3500 | 5.8538 | 0.6465 | 0.749 | 0.7306 | 0.6014 | 0.6486 | 0.6801 | 0.6644 | 0.8092 | 0.8383 | 0.6448 | 0.8784 | 0.9506 | 0.6314 | 0.7432 | 0.4033 | 0.446 | -1.0 | -1.0 | 0.6013 | 0.6536 | 0.9291 | 0.9609 | 0.729 | 0.9151 | 0.64 | 0.9942 | 0.9361 | 0.9592 | 0.8382 | 0.9396 | 0.1472 | 0.893 | 0.7465 | 0.8059 | 0.803 | 0.952 | 0.0352 | 0.7725 | 0.8569 | 0.892 | 0.7539 | 0.8091 |
| 5.4748 | 5.0 | 4375 | 5.8922 | 0.6416 | 0.7467 | 0.7203 | 0.5884 | 0.6359 | 0.7155 | 0.6543 | 0.8104 | 0.8498 | 0.6337 | 0.895 | 0.965 | 0.6726 | 0.7857 | 0.3624 | 0.4249 | -1.0 | -1.0 | 0.5819 | 0.641 | 0.9266 | 0.9518 | 0.6374 | 0.9073 | 0.7673 | 0.9959 | 0.9372 | 0.9692 | 0.9629 | 0.9861 | 0.0824 | 0.9316 | 0.7745 | 0.8198 | 0.7208 | 0.9508 | 0.015 | 0.8404 | 0.7927 | 0.8863 | 0.7489 | 0.8061 |
| 5.352 | 6.0 | 5250 | 6.3142 | 0.6409 | 0.741 | 0.7178 | 0.6313 | 0.5958 | 0.6861 | 0.6395 | 0.7987 | 0.8411 | 0.6741 | 0.8595 | 0.9544 | 0.7148 | 0.8051 | 0.4792 | 0.5386 | -1.0 | -1.0 | 0.5879 | 0.6392 | 0.9101 | 0.9478 | 0.5811 | 0.8811 | 0.8213 | 0.9987 | 0.9139 | 0.96 | 0.9479 | 0.9767 | 0.052 | 0.9105 | 0.7854 | 0.8321 | 0.6801 | 0.9332 | 0.0209 | 0.6995 | 0.7664 | 0.8798 | 0.7112 | 0.7733 |
| 5.1405 | 7.0 | 6125 | 6.3704 | 0.6464 | 0.7579 | 0.7185 | 0.5862 | 0.6209 | 0.6971 | 0.6346 | 0.7737 | 0.8247 | 0.6346 | 0.865 | 0.9372 | 0.6828 | 0.7855 | 0.5464 | 0.5882 | -1.0 | -1.0 | 0.5058 | 0.5697 | 0.9172 | 0.9526 | 0.6302 | 0.9021 | 0.8037 | 0.9908 | 0.9339 | 0.9643 | 0.9615 | 0.9767 | 0.0249 | 0.6942 | 0.7298 | 0.7859 | 0.7449 | 0.9567 | 0.0025 | 0.713 | 0.8942 | 0.9397 | 0.6718 | 0.7272 |
| 4.9059 | 8.0 | 7000 | 6.4879 | 0.6238 | 0.7487 | 0.6799 | 0.5369 | 0.6248 | 0.6895 | 0.6205 | 0.7655 | 0.8153 | 0.5983 | 0.8634 | 0.9561 | 0.6155 | 0.7369 | 0.4736 | 0.5339 | -1.0 | -1.0 | 0.4614 | 0.5228 | 0.9159 | 0.9516 | 0.6436 | 0.8783 | 0.867 | 0.9951 | 0.9482 | 0.9714 | 0.9574 | 0.9767 | 0.0317 | 0.806 | 0.6742 | 0.7409 | 0.6387 | 0.9321 | 0.0037 | 0.7368 | 0.8625 | 0.9115 | 0.6398 | 0.7199 |
| 4.8446 | 9.0 | 7875 | 6.5165 | 0.6064 | 0.734 | 0.6583 | 0.5305 | 0.6055 | 0.6766 | 0.6057 | 0.7479 | 0.8021 | 0.5908 | 0.8441 | 0.9489 | 0.598 | 0.7249 | 0.4926 | 0.5547 | -1.0 | -1.0 | 0.4304 | 0.4976 | 0.9153 | 0.9478 | 0.5701 | 0.8526 | 0.7636 | 0.9867 | 0.9459 | 0.9684 | 0.9583 | 0.9762 | 0.0271 | 0.7862 | 0.6457 | 0.7131 | 0.6268 | 0.9338 | 0.0014 | 0.6518 | 0.8662 | 0.914 | 0.6484 | 0.7213 |
| 4.7656 | 10.0 | 8750 | 6.4174 | 0.6051 | 0.7291 | 0.6597 | 0.5252 | 0.6131 | 0.6723 | 0.6006 | 0.7415 | 0.7959 | 0.5831 | 0.8299 | 0.948 | 0.5841 | 0.7219 | 0.4357 | 0.4955 | -1.0 | -1.0 | 0.4363 | 0.5014 | 0.9189 | 0.9512 | 0.5995 | 0.8773 | 0.7881 | 0.9906 | 0.9485 | 0.9752 | 0.9582 | 0.9782 | 0.0257 | 0.777 | 0.6432 | 0.7154 | 0.5994 | 0.9274 | 0.0019 | 0.5736 | 0.8665 | 0.9243 | 0.6647 | 0.7338 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0
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