--- 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-b16-lr1e-4 results: [] --- # suas-2025-rtdetr-finetuned-b16-lr1e-4 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: 7.4789 - Map: 0.636 - Map 50: 0.7833 - Map 75: 0.6825 - Map Small: 0.5292 - Map Medium: 0.6761 - Map Large: 0.6243 - Mar 1: 0.6914 - Mar 10: 0.8319 - Mar 100: 0.8459 - Mar Small: 0.6526 - Mar Medium: 0.861 - Mar Large: 0.9511 - Map Baseball-bat: 0.7327 - Mar 100 Baseball-bat: 0.8479 - Map Basketball: 0.7378 - Mar 100 Basketball: 0.8453 - Map Car: -1.0 - Mar 100 Car: -1.0 - Map Football: 0.515 - Mar 100 Football: 0.5874 - Map Human: 0.2845 - Mar 100 Human: 0.6493 - Map Luggage: 0.3927 - Mar 100 Luggage: 0.9199 - Map Mattress: 0.4605 - Mar 100 Mattress: 0.9731 - Map Motorcycle: 0.8921 - Mar 100 Motorcycle: 0.9379 - Map Skis: 0.9356 - Mar 100 Skis: 0.9772 - Map Snowboard: 0.4858 - Mar 100 Snowboard: 0.9648 - Map Soccer-ball: 0.291 - Mar 100 Soccer-ball: 0.5365 - Map Stop-sign: 0.8721 - Mar 100 Stop-sign: 0.9777 - Map Tennis-racket: 0.8872 - Mar 100 Tennis-racket: 0.9275 - Map Umbrella: 0.7926 - Mar 100 Umbrella: 0.945 - Map Volleyball: 0.6243 - Mar 100 Volleyball: 0.7525 ## 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: 16 - eval_batch_size: 16 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:----------------:|:--------------------:|:--------------:|:------------------:|:-------:|:-----------:|:------------:|:----------------:|:---------:|:-------------:|:-----------:|:---------------:|:------------:|:----------------:|:--------------:|:------------------:|:--------:|:------------:|:-------------:|:-----------------:|:---------------:|:-------------------:|:-------------:|:-----------------:|:-----------------:|:---------------------:|:------------:|:----------------:|:--------------:|:------------------:| | 14.9473 | 1.0 | 438 | 6.0393 | 0.6929 | 0.8006 | 0.7826 | 0.6924 | 0.6693 | 0.7428 | 0.7205 | 0.8507 | 0.8584 | 0.7888 | 0.8574 | 0.8979 | 0.7927 | 0.8492 | 0.8387 | 0.9263 | -1.0 | -1.0 | 0.5419 | 0.8203 | 0.2032 | 0.5136 | 0.4715 | 0.931 | 0.6218 | 0.9887 | 0.866 | 0.9199 | 0.8182 | 0.8782 | 0.5796 | 0.9515 | 0.6169 | 0.6753 | 0.9283 | 0.9732 | 0.814 | 0.8668 | 0.8116 | 0.8802 | 0.7961 | 0.8431 | | 6.8045 | 2.0 | 876 | 5.2885 | 0.7468 | 0.8307 | 0.8234 | 0.7611 | 0.7249 | 0.6791 | 0.7565 | 0.8986 | 0.9052 | 0.8475 | 0.877 | 0.9424 | 0.8403 | 0.8959 | 0.8533 | 0.9358 | -1.0 | -1.0 | 0.7497 | 0.8638 | 0.1319 | 0.532 | 0.4374 | 0.9456 | 0.5892 | 0.9958 | 0.9306 | 0.9599 | 0.9652 | 0.9802 | 0.6246 | 0.995 | 0.7541 | 0.8175 | 0.9788 | 0.9958 | 0.8972 | 0.9316 | 0.9091 | 0.9701 | 0.7936 | 0.8532 | | 6.122 | 3.0 | 1314 | 6.0592 | 0.7128 | 0.8135 | 0.7987 | 0.6996 | 0.7187 | 0.665 | 0.739 | 0.8748 | 0.8891 | 0.8104 | 0.8731 | 0.9347 | 0.8141 | 0.8864 | 0.8214 | 0.9197 | -1.0 | -1.0 | 0.7119 | 0.8346 | 0.1712 | 0.5716 | 0.3367 | 0.9129 | 0.4596 | 0.9784 | 0.9166 | 0.9472 | 0.9445 | 0.9718 | 0.6822 | 0.9922 | 0.6055 | 0.7355 | 0.9341 | 0.9659 | 0.8819 | 0.9207 | 0.9083 | 0.9698 | 0.7906 | 0.8409 | | 5.7542 | 4.0 | 1752 | 6.7229 | 0.7089 | 0.8239 | 0.7984 | 0.6721 | 0.7153 | 0.6452 | 0.752 | 0.8732 | 0.8817 | 0.7591 | 0.8769 | 0.9587 | 0.7857 | 0.8574 | 0.835 | 0.9052 | -1.0 | -1.0 | 0.6519 | 0.7566 | 0.3228 | 0.6242 | 0.5068 | 0.9147 | 0.4086 | 0.9842 | 0.9254 | 0.9588 | 0.9363 | 0.9639 | 0.6089 | 0.9904 | 0.6176 | 0.7185 | 0.8906 | 0.9765 | 0.8719 | 0.9197 | 0.8104 | 0.9656 | 0.7532 | 0.8083 | | 5.4197 | 5.0 | 2190 | 6.9705 | 0.6705 | 0.7896 | 0.7393 | 0.6309 | 0.6775 | 0.6347 | 0.7021 | 0.8427 | 0.8567 | 0.7156 | 0.8715 | 0.9375 | 0.8044 | 0.874 | 0.8447 | 0.8896 | -1.0 | -1.0 | 0.6092 | 0.6991 | 0.1786 | 0.5444 | 0.3152 | 0.8589 | 0.3916 | 0.946 | 0.943 | 0.9708 | 0.957 | 0.9782 | 0.521 | 0.9759 | 0.4336 | 0.5923 | 0.8949 | 0.9757 | 0.8747 | 0.9171 | 0.8842 | 0.9704 | 0.7354 | 0.8017 | | 5.1598 | 6.0 | 2628 | 6.7201 | 0.6509 | 0.7806 | 0.7067 | 0.5621 | 0.6931 | 0.6332 | 0.6927 | 0.826 | 0.839 | 0.6904 | 0.8633 | 0.8993 | 0.7365 | 0.8412 | 0.8438 | 0.9021 | -1.0 | -1.0 | 0.5378 | 0.639 | 0.1533 | 0.4137 | 0.412 | 0.91 | 0.3546 | 0.9008 | 0.9302 | 0.967 | 0.9724 | 0.9881 | 0.5606 | 0.9739 | 0.3666 | 0.5918 | 0.8779 | 0.9642 | 0.843 | 0.8969 | 0.8855 | 0.9633 | 0.6379 | 0.7946 | | 5.1626 | 7.0 | 3066 | 7.2974 | 0.644 | 0.7767 | 0.6936 | 0.5262 | 0.6787 | 0.6513 | 0.7043 | 0.8445 | 0.8587 | 0.6746 | 0.8942 | 0.9601 | 0.7639 | 0.8663 | 0.7068 | 0.8149 | -1.0 | -1.0 | 0.5452 | 0.6602 | 0.3086 | 0.6825 | 0.41 | 0.9299 | 0.4634 | 0.9747 | 0.9327 | 0.9679 | 0.9505 | 0.9797 | 0.4214 | 0.9677 | 0.2936 | 0.5483 | 0.852 | 0.9818 | 0.8823 | 0.9181 | 0.8924 | 0.9678 | 0.5933 | 0.7615 | | 5.15 | 8.0 | 3504 | 7.2201 | 0.6572 | 0.7908 | 0.7113 | 0.5559 | 0.6962 | 0.6276 | 0.7048 | 0.8396 | 0.8534 | 0.6795 | 0.8702 | 0.9327 | 0.7574 | 0.8526 | 0.7803 | 0.8718 | -1.0 | -1.0 | 0.5741 | 0.6547 | 0.2742 | 0.5961 | 0.418 | 0.9233 | 0.4893 | 0.9697 | 0.91 | 0.9479 | 0.9345 | 0.9807 | 0.5642 | 0.9692 | 0.3294 | 0.5602 | 0.8803 | 0.9821 | 0.8794 | 0.9264 | 0.7936 | 0.9476 | 0.616 | 0.7659 | | 4.9521 | 9.0 | 3942 | 7.5060 | 0.6374 | 0.7847 | 0.6838 | 0.5132 | 0.6921 | 0.6331 | 0.6868 | 0.8276 | 0.8419 | 0.6487 | 0.8619 | 0.9341 | 0.7388 | 0.8431 | 0.715 | 0.8273 | -1.0 | -1.0 | 0.5194 | 0.592 | 0.2577 | 0.624 | 0.3945 | 0.9234 | 0.4599 | 0.966 | 0.9031 | 0.942 | 0.9235 | 0.9752 | 0.5532 | 0.9694 | 0.2822 | 0.5368 | 0.9131 | 0.9855 | 0.8769 | 0.9244 | 0.8091 | 0.9438 | 0.5766 | 0.7336 | | 4.8252 | 10.0 | 4380 | 7.4789 | 0.636 | 0.7833 | 0.6825 | 0.5292 | 0.6761 | 0.6243 | 0.6914 | 0.8319 | 0.8459 | 0.6526 | 0.861 | 0.9511 | 0.7327 | 0.8479 | 0.7378 | 0.8453 | -1.0 | -1.0 | 0.515 | 0.5874 | 0.2845 | 0.6493 | 0.3927 | 0.9199 | 0.4605 | 0.9731 | 0.8921 | 0.9379 | 0.9356 | 0.9772 | 0.4858 | 0.9648 | 0.291 | 0.5365 | 0.8721 | 0.9777 | 0.8872 | 0.9275 | 0.7926 | 0.945 | 0.6243 | 0.7525 | ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.21.0