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queue_detection

Object Detection
Transformers
Safetensors
conditional_detr
Generated from Trainer
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metadata
base_model: microsoft/conditional-detr-resnet-50
license: apache-2.0
tags:
  - generated_from_trainer
model-index:
  - name: queue_detection
    results: []

queue_detection

This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2068
  • Map: 0.9189
  • Map 50: 0.9898
  • Map 75: 0.9755
  • Map Small: -1.0
  • Map Medium: 0.1871
  • Map Large: 0.9234
  • Mar 1: 0.49
  • Mar 10: 0.9447
  • Mar 100: 0.9466
  • Mar Small: -1.0
  • Mar Medium: 0.2615
  • Mar Large: 0.9505
  • Map Cashier: 0.935
  • Mar 100 Cashier: 0.9592
  • Map Cx: 0.9027
  • Mar 100 Cx: 0.9341

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: 2
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • num_epochs: 30
  • mixed_precision_training: Native AMP

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 Cashier Mar 100 Cashier Map Cx Mar 100 Cx
No log 1.0 218 1.3811 0.2363 0.4099 0.2515 -1.0 0.0 0.2376 0.2364 0.5459 0.719 -1.0 0.0 0.7238 0.2986 0.7856 0.174 0.6523
No log 2.0 436 0.9194 0.575 0.8212 0.6661 -1.0 0.038 0.5796 0.3518 0.7412 0.7921 -1.0 0.1 0.7978 0.6716 0.8368 0.4783 0.7473
2.4678 3.0 654 0.6968 0.6863 0.9358 0.8313 -1.0 0.0679 0.6906 0.3901 0.7636 0.7806 -1.0 0.12 0.7848 0.7373 0.8132 0.6354 0.7481
2.4678 4.0 872 0.6535 0.7043 0.9566 0.8452 -1.0 0.0421 0.7091 0.3916 0.7748 0.7871 -1.0 0.1615 0.7909 0.772 0.8416 0.6366 0.7326
0.723 5.0 1090 0.5906 0.7447 0.9667 0.8788 -1.0 0.0367 0.7494 0.4113 0.8026 0.8122 -1.0 0.0955 0.8166 0.7913 0.8551 0.6981 0.7693
0.723 6.0 1308 0.5508 0.7473 0.9631 0.893 0.0 0.0672 0.7527 0.4199 0.8021 0.8067 0.0 0.1077 0.812 0.7943 0.8428 0.7002 0.7705
0.5955 7.0 1526 0.5073 0.7626 0.9656 0.9062 -1.0 0.0515 0.7673 0.423 0.8156 0.8182 -1.0 0.2273 0.8226 0.7917 0.8476 0.7335 0.7887
0.5955 8.0 1744 0.5195 0.7452 0.9721 0.8832 -1.0 0.0559 0.7511 0.417 0.8022 0.8105 -1.0 0.1607 0.8149 0.7925 0.8448 0.6979 0.7762
0.5955 9.0 1962 0.4328 0.8028 0.9786 0.938 -1.0 0.0481 0.808 0.441 0.8515 0.8573 -1.0 0.1154 0.8625 0.8348 0.8846 0.7708 0.8299
0.5055 10.0 2180 0.4195 0.8085 0.9847 0.944 0.0 0.0616 0.8129 0.4365 0.8526 0.8591 0.0 0.1111 0.8635 0.8241 0.8793 0.7929 0.839
0.5055 11.0 2398 0.4155 0.8145 0.9803 0.9465 0.0 0.0555 0.8236 0.4455 0.8556 0.8598 0.0 0.1 0.8691 0.8373 0.8813 0.7918 0.8383
0.457 12.0 2616 0.3886 0.8301 0.984 0.9549 0.0 0.1278 0.8355 0.4513 0.8691 0.8731 0.0 0.1625 0.878 0.8596 0.9024 0.8006 0.8438
0.457 13.0 2834 0.3750 0.829 0.9878 0.9563 -1.0 0.1024 0.8333 0.4508 0.8692 0.8735 -1.0 0.17 0.8772 0.8507 0.894 0.8073 0.8531
0.3971 14.0 3052 0.3571 0.8427 0.9871 0.9524 -1.0 0.0895 0.849 0.4576 0.8803 0.8843 -1.0 0.1531 0.8903 0.8627 0.9071 0.8227 0.8615
0.3971 15.0 3270 0.3637 0.8398 0.983 0.9587 0.0 0.0682 0.8481 0.4565 0.8754 0.8779 0.0 0.0972 0.8867 0.8692 0.9062 0.8104 0.8496
0.3971 16.0 3488 0.3373 0.8494 0.9894 0.9514 -1.0 0.2611 0.8534 0.4653 0.8849 0.8883 -1.0 0.3929 0.8923 0.8756 0.9121 0.8233 0.8645
0.387 17.0 3706 0.2885 0.8744 0.9895 0.9712 -1.0 0.1702 0.8786 0.472 0.9076 0.9083 -1.0 0.2269 0.9124 0.8951 0.927 0.8537 0.8897
0.387 18.0 3924 0.2835 0.8737 0.9893 0.9742 -1.0 0.1473 0.8773 0.4732 0.9064 0.9085 -1.0 0.1833 0.9119 0.8977 0.9287 0.8498 0.8883
0.3216 19.0 4142 0.2699 0.8816 0.9894 0.9722 -1.0 0.1445 0.8865 0.4786 0.9123 0.9142 -1.0 0.3286 0.9192 0.9065 0.9364 0.8568 0.8919
0.3216 20.0 4360 0.2711 0.8813 0.9895 0.9727 -1.0 0.1181 0.8858 0.4775 0.914 0.9156 -1.0 0.1917 0.9199 0.9041 0.9348 0.8584 0.8963
0.3117 21.0 4578 0.2489 0.8889 0.9897 0.9763 -1.0 0.2022 0.8934 0.4817 0.9215 0.9228 -1.0 0.25 0.9261 0.911 0.9415 0.8668 0.9041
0.3117 22.0 4796 0.2739 0.8854 0.9846 0.9685 0.0 0.1351 0.8944 0.4797 0.9132 0.9159 0.0 0.2059 0.9244 0.9071 0.9351 0.8638 0.8968
0.2812 23.0 5014 0.2489 0.8989 0.9895 0.9702 -1.0 0.188 0.9023 0.482 0.9246 0.9269 -1.0 0.2536 0.9309 0.9183 0.9419 0.8795 0.9118
0.2812 24.0 5232 0.2267 0.9059 0.9897 0.9829 -1.0 0.2171 0.9077 0.4861 0.9328 0.9341 -1.0 0.2818 0.9371 0.9253 0.9488 0.8864 0.9193
0.2812 25.0 5450 0.2326 0.9057 0.9899 0.9758 -1.0 0.1995 0.9097 0.4837 0.9316 0.9326 -1.0 0.2286 0.9371 0.9196 0.9449 0.8917 0.9202
0.26 26.0 5668 0.2111 0.915 0.9899 0.9792 0.0 0.2832 0.9199 0.4902 0.9408 0.9423 0.0 0.415 0.9457 0.9267 0.9521 0.9032 0.9325
0.26 27.0 5886 0.2184 0.9132 0.9898 0.9764 -1.0 0.2546 0.9167 0.4872 0.9375 0.9389 -1.0 0.2767 0.943 0.9277 0.95 0.8988 0.9279
0.2498 28.0 6104 0.2200 0.9159 0.9892 0.9756 -1.0 0.1921 0.9197 0.4872 0.9397 0.9413 -1.0 0.275 0.9446 0.9314 0.9547 0.9004 0.9279
0.2498 29.0 6322 0.2075 0.9209 0.9899 0.9791 -1.0 0.317 0.9241 0.4904 0.9448 0.9454 -1.0 0.34 0.9475 0.9345 0.9565 0.9073 0.9342
0.2519 30.0 6540 0.2068 0.9189 0.9898 0.9755 -1.0 0.1871 0.9234 0.49 0.9447 0.9466 -1.0 0.2615 0.9505 0.935 0.9592 0.9027 0.9341

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1