Rtdetr v2 R50 Finetune 26

--- library_name: transformers license: apache-2.0 base_model: PekingU/rtdetr_v2_r50vd tags: - generated_from_trainer model-index: - name: rtdetr-v2-r50-finetune-26 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-26 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: 6.0635 - Map: 0.5527 - Map 50: 0.8316 - Map 75: 0.6801 - Map Small: 0.5136 - Map Medium: 0.6695 - Map Large: -1.0 - Mar 1: 0.3398 - Mar 10: 0.6634 - Mar 100: 0.7117 - Mar Small: 0.682 - Mar Medium: 0.7874 - Mar Large: -1.0 - Map Artemia: 0.5527 - Mar 100 Artemia: 0.7117 ## 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: 30 ### 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 | 12.2175 | 0.2509 | 0.4704 | 0.262 | 0.1874 | 0.4289 | -1.0 | 0.2888 | 0.5826 | 0.6548 | 0.586 | 0.7489 | -1.0 | 0.2509 | 0.6548 | | 217.6859 | 2.0 | 500 | 8.9410 | 0.434 | 0.8083 | 0.4068 | 0.3634 | 0.5374 | -1.0 | 0.3368 | 0.5841 | 0.6243 | 0.5823 | 0.6832 | -1.0 | 0.434 | 0.6243 | | 217.6859 | 3.0 | 750 | 7.7992 | 0.4882 | 0.8365 | 0.4996 | 0.4175 | 0.5868 | -1.0 | 0.3748 | 0.6143 | 0.6539 | 0.6016 | 0.7263 | -1.0 | 0.4882 | 0.6539 | | 14.0835 | 4.0 | 1000 | 7.6803 | 0.4744 | 0.843...