--- 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-e10-b32-lr1e-5 results: [] --- # suas-2025-rtdetr-finetuned-e10-b32-lr1e-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: 5.7755 - Map: 0.8259 - Map 50: 0.9053 - Map 75: 0.8962 - Map Small: 0.7396 - Map Medium: 0.8978 - Map Large: 0.8904 - Mar 1: 0.7897 - Mar 10: 0.9079 - Mar 100: 0.911 - Mar Small: 0.8045 - Mar Medium: 0.9449 - Mar Large: 0.974 - Map Baseball-bat: 0.8535 - Mar 100 Baseball-bat: 0.9223 - Map Basketball: 0.8181 - Mar 100 Basketball: 0.9213 - Map Car: -1.0 - Mar 100 Car: -1.0 - Map Football: 0.4419 - Mar 100 Football: 0.516 - Map Human: 0.8513 - Mar 100 Human: 0.9473 - Map Luggage: 0.8768 - Mar 100 Luggage: 0.9398 - Map Mattress: 0.5281 - Mar 100 Mattress: 0.9959 - Map Motorcycle: 0.9311 - Mar 100 Motorcycle: 0.9635 - Map Skis: 0.9687 - Mar 100 Skis: 0.9975 - Map Snowboard: 0.9779 - Mar 100 Snowboard: 0.9953 - Map Soccer-ball: 0.8253 - Mar 100 Soccer-ball: 0.8679 - Map Stop-sign: 0.9582 - Mar 100 Stop-sign: 0.9838 - Map Tennis-racket: 0.8887 - Mar 100 Tennis-racket: 0.9275 - Map Umbrella: 0.8417 - Mar 100 Umbrella: 0.9128 - Map Volleyball: 0.8015 - Mar 100 Volleyball: 0.8632 ## 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: 32 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:----------------:|:--------------------:|:--------------:|:------------------:|:-------:|:-----------:|:------------:|:----------------:|:---------:|:-------------:|:-----------:|:---------------:|:------------:|:----------------:|:--------------:|:------------------:|:--------:|:------------:|:-------------:|:-----------------:|:---------------:|:-------------------:|:-------------:|:-----------------:|:-----------------:|:---------------------:|:------------:|:----------------:|:--------------:|:------------------:| | 31.6046 | 1.0 | 219 | 12.5434 | 0.3553 | 0.4457 | 0.3805 | 0.2717 | 0.4559 | 0.4479 | 0.5615 | 0.7432 | 0.7718 | 0.5413 | 0.873 | 0.9496 | 0.6878 | 0.9023 | 0.0337 | 0.5289 | -1.0 | -1.0 | 0.1746 | 0.4829 | 0.4718 | 0.8344 | 0.1939 | 0.8512 | 0.139 | 0.9643 | 0.5451 | 0.9288 | 0.9133 | 0.9891 | 0.4051 | 0.9817 | 0.3034 | 0.4481 | 0.3931 | 0.9455 | 0.3464 | 0.615 | 0.2395 | 0.8765 | 0.1274 | 0.4561 | | 11.2376 | 2.0 | 438 | 8.7086 | 0.7492 | 0.8518 | 0.8382 | 0.6809 | 0.8229 | 0.7802 | 0.7521 | 0.8824 | 0.8898 | 0.7682 | 0.9257 | 0.9652 | 0.8225 | 0.9069 | 0.7335 | 0.8417 | -1.0 | -1.0 | 0.5246 | 0.6187 | 0.8272 | 0.9075 | 0.8351 | 0.91 | 0.2917 | 0.9978 | 0.8814 | 0.9449 | 0.9763 | 0.9916 | 0.9795 | 0.9963 | 0.7605 | 0.8211 | 0.9176 | 0.9746 | 0.8631 | 0.9088 | 0.4335 | 0.9109 | 0.6428 | 0.7265 | | 7.5378 | 3.0 | 657 | 7.5895 | 0.7926 | 0.8861 | 0.874 | 0.7165 | 0.8732 | 0.8452 | 0.7772 | 0.8976 | 0.9033 | 0.7944 | 0.9368 | 0.9808 | 0.8325 | 0.9096 | 0.7675 | 0.8908 | -1.0 | -1.0 | 0.5341 | 0.6171 | 0.8536 | 0.9323 | 0.8621 | 0.9288 | 0.3232 | 0.9995 | 0.9099 | 0.9534 | 0.9589 | 0.9851 | 0.9779 | 0.9968 | 0.7949 | 0.8422 | 0.9557 | 0.9799 | 0.874 | 0.9187 | 0.7466 | 0.908 | 0.7061 | 0.7833 | | 6.7821 | 4.0 | 876 | 6.9548 | 0.7906 | 0.8773 | 0.8662 | 0.6944 | 0.8658 | 0.8692 | 0.7707 | 0.8882 | 0.8931 | 0.763 | 0.9347 | 0.9677 | 0.8386 | 0.9153 | 0.738 | 0.8742 | -1.0 | -1.0 | 0.4264 | 0.5058 | 0.83 | 0.9254 | 0.8657 | 0.9297 | 0.4204 | 0.9995 | 0.9153 | 0.9537 | 0.955 | 0.9891 | 0.9727 | 0.9924 | 0.8047 | 0.8542 | 0.9495 | 0.9869 | 0.8817 | 0.9254 | 0.7954 | 0.8976 | 0.6753 | 0.7549 | | 6.4517 | 5.0 | 1095 | 6.2876 | 0.8217 | 0.9052 | 0.8965 | 0.7345 | 0.8867 | 0.884 | 0.7885 | 0.9066 | 0.9107 | 0.8055 | 0.9448 | 0.9705 | 0.8384 | 0.92 | 0.7994 | 0.9156 | -1.0 | -1.0 | 0.4948 | 0.5703 | 0.8404 | 0.9363 | 0.8765 | 0.9391 | 0.5335 | 0.9946 | 0.927 | 0.9651 | 0.9504 | 0.995 | 0.9766 | 0.9947 | 0.8183 | 0.8602 | 0.946 | 0.9813 | 0.8881 | 0.9259 | 0.8387 | 0.9205 | 0.7759 | 0.8306 | | 6.1454 | 6.0 | 1314 | 5.8688 | 0.8355 | 0.9186 | 0.9086 | 0.7416 | 0.8939 | 0.9026 | 0.7975 | 0.91 | 0.9135 | 0.8094 | 0.9477 | 0.973 | 0.8577 | 0.9237 | 0.7904 | 0.9206 | -1.0 | -1.0 | 0.4838 | 0.5638 | 0.8723 | 0.9423 | 0.8782 | 0.9378 | 0.6114 | 0.9982 | 0.9296 | 0.9638 | 0.9826 | 0.9965 | 0.9772 | 0.9953 | 0.8218 | 0.8638 | 0.9541 | 0.9866 | 0.887 | 0.9285 | 0.851 | 0.9228 | 0.7993 | 0.8458 | | 6.0567 | 7.0 | 1533 | 5.9641 | 0.8278 | 0.9084 | 0.9005 | 0.7434 | 0.8944 | 0.89 | 0.7917 | 0.9098 | 0.9135 | 0.8091 | 0.9467 | 0.9854 | 0.8531 | 0.9224 | 0.7819 | 0.9187 | -1.0 | -1.0 | 0.4727 | 0.5437 | 0.8434 | 0.9498 | 0.8774 | 0.9406 | 0.5438 | 0.9946 | 0.9232 | 0.963 | 0.9791 | 0.998 | 0.9771 | 0.994 | 0.8241 | 0.8707 | 0.9609 | 0.9785 | 0.8909 | 0.9264 | 0.8533 | 0.9254 | 0.8084 | 0.863 | | 5.9579 | 8.0 | 1752 | 5.7184 | 0.8339 | 0.9141 | 0.9056 | 0.7448 | 0.8939 | 0.9004 | 0.7947 | 0.9099 | 0.9136 | 0.8088 | 0.9453 | 0.9871 | 0.8529 | 0.9222 | 0.8083 | 0.9175 | -1.0 | -1.0 | 0.4795 | 0.5478 | 0.8608 | 0.9444 | 0.8795 | 0.9406 | 0.5759 | 0.998 | 0.9325 | 0.9641 | 0.9711 | 0.9975 | 0.9816 | 0.9964 | 0.8218 | 0.8661 | 0.9655 | 0.9872 | 0.8942 | 0.928 | 0.8474 | 0.9251 | 0.8034 | 0.8551 | | 5.9419 | 9.0 | 1971 | 5.7328 | 0.8305 | 0.9091 | 0.9018 | 0.7434 | 0.8955 | 0.8956 | 0.7924 | 0.9098 | 0.9136 | 0.8101 | 0.9462 | 0.9868 | 0.858 | 0.9253 | 0.8223 | 0.9197 | -1.0 | -1.0 | 0.4625 | 0.5377 | 0.8502 | 0.9471 | 0.8804 | 0.9409 | 0.5395 | 0.9939 | 0.93 | 0.9619 | 0.9787 | 0.9975 | 0.979 | 0.9959 | 0.8296 | 0.8704 | 0.9624 | 0.988 | 0.8852 | 0.9269 | 0.8393 | 0.9164 | 0.8101 | 0.8684 | | 5.9176 | 10.0 | 2190 | 5.7755 | 0.8259 | 0.9053 | 0.8962 | 0.7396 | 0.8978 | 0.8904 | 0.7897 | 0.9079 | 0.911 | 0.8045 | 0.9449 | 0.974 | 0.8535 | 0.9223 | 0.8181 | 0.9213 | -1.0 | -1.0 | 0.4419 | 0.516 | 0.8513 | 0.9473 | 0.8768 | 0.9398 | 0.5281 | 0.9959 | 0.9311 | 0.9635 | 0.9687 | 0.9975 | 0.9779 | 0.9953 | 0.8253 | 0.8679 | 0.9582 | 0.9838 | 0.8887 | 0.9275 | 0.8417 | 0.9128 | 0.8015 | 0.8632 | ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.21.0