Rtdetr v2 R50 Finetune 19

--- library_name: transformers license: apache-2.0 base_model: PekingU/rtdetr_v2_r50vd tags: - generated_from_trainer model-index: - name: rtdetr-v2-r50-finetune-19 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. --> # rtdetr-v2-r50-finetune-19 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: 5.7893 - Map: 0.5958 - Map 50: 0.913 - Map 75: 0.6854 - Map Small: 0.5668 - Map Medium: 0.6783 - Map Large: -1.0 - Mar 1: 0.346 - Mar 10: 0.6715 - Mar 100: 0.7071 - Mar Small: 0.6784 - Mar Medium: 0.7805 - Mar Large: -1.0 - Map Artemia: 0.5958 - Mar 100 Artemia: 0.7071 ## 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 OptimizerNames.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: 50 ### 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 | 15.4471 | 0.4602 | 0.8135 | 0.4712 | 0.36 | 0.5794 | -1.0 | 0.3551 | 0.6012 | 0.6505 | 0.5677 | 0.7642 | -1.0 | 0.4602 | 0.6505 | | 294.1038 | 2.0 | 500 | 8.2104 | 0.4944 | 0.8664 | 0.5264 | 0.425 | 0.589 | -1.0 | 0.3723 | 0.585 | 0.6355 | 0.5801 | 0.7124 | -1.0 | 0.4944 | 0.6355 | | 294.1038 | 3.0 | 750 | 7.5714 | 0.5041 | 0.8852 | 0.5233 | 0.4306 | 0.6117 | -1.0 | 0.3813 | 0.5969 | 0.6523 | 0.5855 | 0.7453 | -1.0 | 0.5041 | 0.6523 | | 13.5778 | 4.0 | 1000 | 7.5424 | 0.5045 | 0.8843...