Rtdetr v2 R101 Final

--- library_name: transformers license: apache-2.0 base_model: PekingU/rtdetr_v2_r101vd tags: - generated_from_trainer model-index: - name: rtdetr-v2-r101-final 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-r101-final This model is a fine-tuned version of [PekingU/rtdetr_v2_r101vd](https://huggingface.co/PekingU/rtdetr_v2_r101vd) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.8929 - Map: 0.5152 - Map 50: 0.8401 - Map 75: 0.6058 - Map Small: 0.4759 - Map Medium: 0.6186 - Map Large: -1.0 - Mar 1: 0.3149 - Mar 10: 0.612 - Mar 100: 0.612 - Mar Small: 0.5802 - Mar Medium: 0.6931 - Mar Large: -1.0 - Map Artemia: 0.5152 - Mar 100 Artemia: 0.612 ## 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: 80 ### 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 | 14.4631 | 0.4356 | 0.7885 | 0.4451 | 0.3341 | 0.5718 | -1.0 | 0.3442 | 0.5723 | 0.6283 | 0.5349 | 0.7562 | -1.0 | 0.4356 | 0.6283 | | 172.3915 | 2.0 | 500 | 8.3680 | 0.4508 | 0.8175 | 0.4463 | 0.3608 | 0.5821 | -1.0 | 0.3611 | 0.5944 | 0.6374 | 0.5914 | 0.7036 | -1.0 | 0.4508 | 0.6374 | | 172.3915 | 3.0 | 750 | 7.9072 | 0.486 | 0.8594 | 0.5019 | 0.414 | 0.6008 | -1.0 | 0.3741 | 0.6081 | 0.6498 | 0.6086 | 0.7073 | -1.0 | 0.486 | 0.6498 | | 13.7544 | 4.0 | 1000 | 8.1075 | 0.4813 | 0.8567 | 0.4...