law-game-evidence-replacement-finetune
This model is a fine-tuned version of PekingU/rtdetr_r50vd_coco_o365 on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 3.4253
- eval_map: 0.8264
- eval_map_50: 0.8488
- eval_map_75: 0.8441
- eval_map_small: 0.5688
- eval_map_medium: 0.9527
- eval_map_large: 0.8547
- eval_mar_1: 0.7043
- eval_mar_10: 0.9575
- eval_mar_100: 0.9727
- eval_mar_small: 0.5949
- eval_mar_medium: 0.9738
- eval_mar_large: 0.9894
- eval_map_evidence: -1.0
- eval_mar_100_evidence: -1.0
- eval_map_ambulance: 0.9802
- eval_mar_100_ambulance: 0.9899
- eval_map_artificial_target: 0.9267
- eval_mar_100_artificial_target: 0.9572
- eval_map_cartridge: 0.9742
- eval_mar_100_cartridge: 0.9949
- eval_map_gun: 0.9165
- eval_mar_100_gun: 0.9403
- eval_map_knife: 0.8599
- eval_mar_100_knife: 0.931
- eval_map_police: 0.9935
- eval_mar_100_police: 0.9959
- eval_map_traffic: 0.9586
- eval_mar_100_traffic: 0.9726
- eval_map_traffic_cone: 0.0013
- eval_mar_100_traffic_cone: 1.0
- eval_runtime: 50.5267
- eval_samples_per_second: 16.467
- eval_steps_per_second: 2.058
- epoch: 28.0
- step: 5152
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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 50
Framework versions
- Transformers 4.44.0.dev0
- Pytorch 2.3.1+cu121
- Tokenizers 0.19.1
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Model tree for anastasispk/law-game-evidence-replacement-finetune-v1
Base model
PekingU/rtdetr_r50vd_coco_o365
Finetuned
this model