rtdetr-v2-r50-cppe5-finetune-2

This model is a fine-tuned version of PekingU/rtdetr_v2_r50vd on an unknown dataset. It achieves the following results on the evaluation set:

• eval_loss: 56.8776
• eval_map: 0.0016
• eval_map_50: 0.0033
• eval_map_75: 0.0013
• eval_map_small: 0.0001
• eval_map_medium: 0.002
• eval_map_large: 0.0141
• eval_mar_1: 0.0047
• eval_mar_10: 0.0217
• eval_mar_100: 0.0874
• eval_mar_small: 0.0102
• eval_mar_medium: 0.0588
• eval_mar_large: 0.1563
• eval_map_Coverall: 0.0002
• eval_mar_100_Coverall: 0.0821
• eval_map_Face_Shield: 0.0055
• eval_mar_100_Face_Shield: 0.1176
• eval_map_Gloves: 0.002
• eval_mar_100_Gloves: 0.1983
• eval_map_Goggles: 0.0
• eval_mar_100_Goggles: 0.0
• eval_map_Mask: 0.0002
• eval_mar_100_Mask: 0.0392
• eval_runtime: 65.4375
• eval_samples_per_second: 0.443
• eval_steps_per_second: 0.061
• epoch: 0.0748
• step: 8

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: 40

Framework versions

• Transformers 5.12.1
• Pytorch 2.12.1+cu130
• Datasets 5.0.0
• Tokenizers 0.22.2