--- library_name: transformers license: apache-2.0 base_model: PekingU/rtdetr_v2_r50vd tags: - generated_from_trainer model-index: - name: rtdetr-v2-r50-finetune-9 results: [] --- # rtdetr-v2-r50-finetune-9 This model is a fine-tuned version of [PekingU/rtdetr_v2_r50vd](https://huggingface.co/PekingU/rtdetr_v2_r50vd) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.4316 - Map: 0.49 - Map 50: 0.781 - Map 75: 0.5504 - Map Small: 0.4762 - Map Medium: 0.6106 - Map Large: -1.0 - Mar 1: 0.3304 - Mar 10: 0.6631 - Mar 100: 0.69 - Mar Small: 0.6653 - Mar Medium: 0.7529 - Mar Large: -1.0 - Map Artemia: 0.49 - Mar 100 Artemia: 0.69 ## 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_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 20 ### 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 Artemia | Mar 100 Artemia | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:-----------:|:---------------:| | No log | 1.0 | 250 | 13.7176 | 0.2126 | 0.3985 | 0.1975 | 0.1372 | 0.4217 | -1.0 | 0.2682 | 0.5863 | 0.6452 | 0.5554 | 0.7647 | -1.0 | 0.2126 | 0.6452 | | 243.12 | 2.0 | 500 | 8.2150 | 0.3694 | 0.6877 | 0.3618 | 0.297 | 0.4979 | -1.0 | 0.3318 | 0.6031 | 0.6383 | 0.5962 | 0.6964 | -1.0 | 0.3694 | 0.6383 | | 243.12 | 3.0 | 750 | 8.2831 | 0.4746 | 0.8355 | 0.4849 | 0.3978 | 0.583 | -1.0 | 0.3723 | 0.595 | 0.6352 | 0.5891 | 0.6964 | -1.0 | 0.4746 | 0.6352 | | 14.0162 | 4.0 | 1000 | 8.2865 | 0.1154 | 0.2137 | 0.108 | 0.3503 | 0.1702 | -1.0 | 0.3536 | 0.5819 | 0.5885 | 0.5315 | 0.6655 | -1.0 | 0.1154 | 0.5885 | | 14.0162 | 5.0 | 1250 | 8.1979 | 0.3304 | 0.6291 | 0.2988 | 0.3337 | 0.4718 | -1.0 | 0.3526 | 0.581 | 0.5988 | 0.5435 | 0.6734 | -1.0 | 0.3304 | 0.5988 | | 12.2257 | 6.0 | 1500 | 8.0874 | 0.4577 | 0.8447 | 0.4189 | 0.3797 | 0.5843 | -1.0 | 0.3642 | 0.5623 | 0.5685 | 0.4897 | 0.6748 | -1.0 | 0.4577 | 0.5685 | | 12.2257 | 7.0 | 1750 | 8.4536 | 0.4003 | 0.801 | 0.3266 | 0.3084 | 0.561 | -1.0 | 0.3336 | 0.5645 | 0.5776 | 0.5223 | 0.6532 | -1.0 | 0.4003 | 0.5776 | | 11.2516 | 8.0 | 2000 | 8.1441 | 0.4345 | 0.8414 | 0.4042 | 0.3667 | 0.5755 | -1.0 | 0.3645 | 0.5698 | 0.5735 | 0.519 | 0.6482 | -1.0 | 0.4345 | 0.5735 | | 11.2516 | 9.0 | 2250 | 8.9297 | 0.3797 | 0.7669 | 0.3187 | 0.2963 | 0.5666 | -1.0 | 0.3153 | 0.5598 | 0.5645 | 0.5038 | 0.6468 | -1.0 | 0.3797 | 0.5645 | | 10.2227 | 10.0 | 2500 | 8.4697 | 0.4315 | 0.8204 | 0.3933 | 0.3543 | 0.572 | -1.0 | 0.3564 | 0.5639 | 0.5657 | 0.5049 | 0.6489 | -1.0 | 0.4315 | 0.5657 | | 10.2227 | 11.0 | 2750 | 8.8899 | 0.3787 | 0.7271 | 0.3401 | 0.3142 | 0.5382 | -1.0 | 0.3364 | 0.571 | 0.5773 | 0.5098 | 0.6691 | -1.0 | 0.3787 | 0.5773 | | 9.3841 | 12.0 | 3000 | 8.7802 | 0.3904 | 0.7519 | 0.3354 | 0.3059 | 0.5668 | -1.0 | 0.3234 | 0.5601 | 0.5601 | 0.488 | 0.6576 | -1.0 | 0.3904 | 0.5601 | | 9.3841 | 13.0 | 3250 | 9.2236 | 0.3568 | 0.7077 | 0.306 | 0.2682 | 0.563 | -1.0 | 0.3097 | 0.5583 | 0.5598 | 0.4859 | 0.6604 | -1.0 | 0.3568 | 0.5598 | | 8.5284 | 14.0 | 3500 | 9.0471 | 0.3744 | 0.7302 | 0.3176 | 0.2931 | 0.5665 | -1.0 | 0.3125 | 0.562 | 0.562 | 0.4929 | 0.6561 | -1.0 | 0.3744 | 0.562 | | 8.5284 | 15.0 | 3750 | 9.1800 | 0.3691 | 0.7034 | 0.336 | 0.2879 | 0.5633 | -1.0 | 0.3031 | 0.5536 | 0.5536 | 0.4842 | 0.6482 | -1.0 | 0.3691 | 0.5536 | | 7.8078 | 16.0 | 4000 | 8.9976 | 0.4009 | 0.7641 | 0.351 | 0.3165 | 0.5737 | -1.0 | 0.3274 | 0.5505 | 0.5505 | 0.4734 | 0.6547 | -1.0 | 0.4009 | 0.5505 | | 7.8078 | 17.0 | 4250 | 9.6222 | 0.3478 | 0.6749 | 0.3029 | 0.2589 | 0.575 | -1.0 | 0.2988 | 0.5573 | 0.5583 | 0.4875 | 0.654 | -1.0 | 0.3478 | 0.5583 | | 7.0036 | 18.0 | 4500 | 9.3709 | 0.3694 | 0.7027 | 0.3222 | 0.2817 | 0.5676 | -1.0 | 0.3093 | 0.5514 | 0.5523 | 0.4783 | 0.6532 | -1.0 | 0.3694 | 0.5523 | | 7.0036 | 19.0 | 4750 | 9.6374 | 0.3628 | 0.6967 | 0.3224 | 0.2695 | 0.574 | -1.0 | 0.2969 | 0.5545 | 0.5561 | 0.4853 | 0.6525 | -1.0 | 0.3628 | 0.5561 | | 6.3958 | 20.0 | 5000 | 9.7867 | 0.3555 | 0.684 | 0.3096 | 0.2599 | 0.5612 | -1.0 | 0.2941 | 0.5539 | 0.5545 | 0.4821 | 0.6532 | -1.0 | 0.3555 | 0.5545 | ### Framework versions - Transformers 4.57.1 - Pytorch 2.8.0+cu128 - Datasets 4.2.0 - Tokenizers 0.22.1