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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: 1.4822
  • Map: 0.2108
  • Map 50: 0.3072
  • Map 75: 0.2725
  • Map Small: -1.0
  • Map Medium: -1.0
  • Map Large: 0.2219
  • Mar 1: 0.1833
  • Mar 10: 0.4195
  • Mar 100: 0.736
  • Mar Small: -1.0
  • Mar Medium: -1.0
  • Mar Large: 0.736
  • Map Cashier: 0.0544
  • Mar 100 Cashier: 0.8053
  • Map Cx: 0.3672
  • Mar 100 Cx: 0.6667

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 5 38.9601 0.0007 0.0042 0.0 -1.0 -1.0 0.001 0.0 0.0186 0.053 -1.0 -1.0 0.053 0.0004 0.0526 0.001 0.0533
No log 2.0 10 20.3884 0.0053 0.0378 0.001 -1.0 -1.0 0.0119 0.0 0.0133 0.11 -1.0 -1.0 0.11 0.0 0.0 0.0106 0.22
No log 3.0 15 8.6229 0.0257 0.0582 0.0072 -1.0 -1.0 0.0573 0.0333 0.12 0.1737 -1.0 -1.0 0.1737 0.0002 0.0474 0.0512 0.3
No log 4.0 20 5.4986 0.0061 0.02 0.0018 -1.0 -1.0 0.0175 0.0 0.0833 0.1 -1.0 -1.0 0.1 0.0 0.0 0.0122 0.2
No log 5.0 25 3.2810 0.0152 0.0644 0.0 -1.0 -1.0 0.0182 0.0067 0.0933 0.1989 -1.0 -1.0 0.1989 0.004 0.1579 0.0264 0.24
No log 6.0 30 2.4527 0.0174 0.0707 0.0008 -1.0 -1.0 0.0201 0.0067 0.0667 0.3286 -1.0 -1.0 0.3286 0.0076 0.3105 0.0272 0.3467
No log 7.0 35 2.1059 0.0222 0.0715 0.0077 -1.0 0.0 0.0244 0.0233 0.0993 0.4733 -1.0 0.0 0.4857 0.017 0.6 0.0273 0.3467
No log 8.0 40 2.1013 0.035 0.117 0.0054 -1.0 -1.0 0.0354 0.0333 0.0467 0.3653 -1.0 -1.0 0.3653 0.0204 0.6105 0.0497 0.12
No log 9.0 45 1.9401 0.0412 0.1081 0.0058 -1.0 -1.0 0.042 0.0267 0.04 0.3639 -1.0 -1.0 0.3639 0.0184 0.6211 0.064 0.1067
No log 10.0 50 2.2227 0.0071 0.0254 0.0017 -1.0 -1.0 0.0075 0.0 0.0433 0.256 -1.0 -1.0 0.256 0.0044 0.3053 0.0098 0.2067
No log 11.0 55 1.9404 0.0126 0.033 0.01 -1.0 -1.0 0.0127 0.0 0.08 0.333 -1.0 -1.0 0.333 0.0082 0.4526 0.0169 0.2133
No log 12.0 60 1.8410 0.0186 0.0658 0.0078 -1.0 -1.0 0.0187 0.0 0.0733 0.3591 -1.0 -1.0 0.3591 0.0125 0.5316 0.0247 0.1867
No log 13.0 65 1.7476 0.0735 0.1829 0.0136 -1.0 -1.0 0.0736 0.0633 0.1133 0.4335 -1.0 -1.0 0.4335 0.0105 0.4737 0.1366 0.3933
No log 14.0 70 1.8939 0.0634 0.1492 0.0276 -1.0 -1.0 0.0638 0.05 0.2033 0.3719 -1.0 -1.0 0.3719 0.0032 0.2105 0.1236 0.5333
No log 15.0 75 1.7653 0.0535 0.1438 0.0387 -1.0 -1.0 0.0554 0.0964 0.1929 0.4917 -1.0 -1.0 0.4917 0.0103 0.4263 0.0968 0.5571
No log 16.0 80 1.6493 0.0988 0.2205 0.0787 -1.0 -1.0 0.1014 0.0733 0.2419 0.6505 -1.0 -1.0 0.6505 0.0225 0.7211 0.1751 0.58
No log 17.0 85 1.7624 0.1198 0.2464 0.0858 0.0 -1.0 0.1289 0.1167 0.2333 0.6149 0.0 -1.0 0.6352 0.0285 0.6632 0.211 0.5667
No log 18.0 90 1.8609 0.149 0.3181 0.0874 -1.0 -1.0 0.1517 0.13 0.2861 0.5789 -1.0 -1.0 0.5789 0.0403 0.6579 0.2576 0.5
No log 19.0 95 1.6860 0.1455 0.257 0.1026 -1.0 -1.0 0.1497 0.1393 0.2697 0.6496 -1.0 -1.0 0.6496 0.039 0.7421 0.2521 0.5571
No log 20.0 100 1.7541 0.1966 0.3274 0.2313 -1.0 -1.0 0.1994 0.17 0.317 0.6182 -1.0 -1.0 0.6182 0.0379 0.6632 0.3553 0.5733
No log 21.0 105 1.7043 0.1735 0.2572 0.1786 -1.0 0.0 0.1913 0.1821 0.3295 0.6252 -1.0 0.0 0.6472 0.0459 0.6789 0.3012 0.5714
No log 22.0 110 1.5489 0.1959 0.3051 0.264 -1.0 -1.0 0.2039 0.17 0.3568 0.6896 -1.0 -1.0 0.6896 0.046 0.7526 0.3458 0.6267
No log 23.0 115 1.6402 0.166 0.293 0.1791 -1.0 -1.0 0.1773 0.1432 0.2851 0.6496 -1.0 -1.0 0.6496 0.048 0.7526 0.284 0.5467
No log 24.0 120 1.5800 0.188 0.3099 0.1815 -1.0 -1.0 0.1965 0.1791 0.3337 0.693 -1.0 -1.0 0.693 0.0408 0.7526 0.3352 0.6333
No log 25.0 125 1.5566 0.1921 0.3038 0.2227 -1.0 -1.0 0.2037 0.17 0.4244 0.6928 -1.0 -1.0 0.6928 0.0642 0.7789 0.32 0.6067
No log 26.0 130 1.7227 0.2044 0.3337 0.2155 -1.0 -1.0 0.2145 0.1667 0.3319 0.5968 -1.0 -1.0 0.5968 0.0484 0.6737 0.3604 0.52
No log 27.0 135 1.5184 0.2095 0.3161 0.2389 -1.0 -1.0 0.2211 0.1877 0.3163 0.7026 -1.0 -1.0 0.7026 0.0603 0.8053 0.3586 0.6
No log 28.0 140 1.5156 0.2172 0.3273 0.2672 -1.0 -1.0 0.2286 0.2077 0.4402 0.7226 -1.0 -1.0 0.7226 0.0681 0.8053 0.3664 0.64
No log 29.0 145 1.6211 0.1652 0.2652 0.2007 -1.0 -1.0 0.1737 0.15 0.4081 0.677 -1.0 -1.0 0.677 0.0463 0.7474 0.2841 0.6067
No log 30.0 150 1.4822 0.2108 0.3072 0.2725 -1.0 -1.0 0.2219 0.1833 0.4195 0.736 -1.0 -1.0 0.736 0.0544 0.8053 0.3672 0.6667

Framework versions

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