detr_finetuned_cppe5

This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1933
  • Map: 0.0
  • Map 50: 0.0
  • Map 75: 0.0
  • Map Small: 0.0
  • Map Medium: -1.0
  • Map Large: -1.0
  • Mar 1: 0.0
  • Mar 10: 0.0
  • Mar 100: 0.0
  • Mar Small: 0.0
  • Mar Medium: -1.0
  • Mar Large: -1.0
  • Map Coverall: 0.0
  • Mar 100 Coverall: 0.0
  • Map Face Shield: 0.0
  • Mar 100 Face Shield: 0.0
  • Map Gloves: 0.0
  • Mar 100 Gloves: 0.0
  • Map Goggles: 0.0
  • Mar 100 Goggles: 0.0
  • Map Mask: 0.0
  • Mar 100 Mask: 0.0

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: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • 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 Coverall Mar 100 Coverall Map Face Shield Mar 100 Face Shield Map Gloves Mar 100 Gloves Map Goggles Mar 100 Goggles Map Mask Mar 100 Mask
No log 1.0 213 1.7858 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
No log 2.0 426 1.7974 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.612 3.0 639 1.6086 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.612 4.0 852 1.5644 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.3972 5.0 1065 1.4356 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.3972 6.0 1278 1.4547 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.3972 7.0 1491 1.4207 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.2125 8.0 1704 1.3967 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.2125 9.0 1917 1.3162 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.09 10.0 2130 1.3086 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.09 11.0 2343 1.3013 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.9743 12.0 2556 1.2823 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.9743 13.0 2769 1.2798 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.9743 14.0 2982 1.2379 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.8793 15.0 3195 1.2404 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.8793 16.0 3408 1.2136 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.7806 17.0 3621 1.2239 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.7806 18.0 3834 1.2372 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.7053 19.0 4047 1.2269 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.7053 20.0 4260 1.2231 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.7053 21.0 4473 1.2135 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.6369 22.0 4686 1.2037 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.6369 23.0 4899 1.2048 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.5831 24.0 5112 1.1930 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.5831 25.0 5325 1.2022 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.5447 26.0 5538 1.1945 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.5447 27.0 5751 1.1970 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.5447 28.0 5964 1.1923 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.5184 29.0 6177 1.1936 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.5184 30.0 6390 1.1933 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Framework versions

  • Transformers 4.46.0
  • Pytorch 2.2.2
  • Datasets 3.0.2
  • Tokenizers 0.20.1
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