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Vehicle_Detection_Model_Zoom

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Vehicle_Detection_Model_Zoom

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

  • Loss: 0.7673
  • Map: 0.0853
  • Map 50: 0.1613
  • Map 75: 0.0814
  • Map Small: 0.452
  • Map Medium: 0.1098
  • Map Large: 0.0
  • Mar 1: 0.1187
  • Mar 10: 0.1824
  • Mar 100: 0.1934
  • Mar Small: 0.5946
  • Mar Medium: 0.1984
  • Mar Large: 0.0
  • Map Camping car: 0.0353
  • Mar 100 Camping car: 0.35
  • Map Car: 0.4678
  • Mar 100 Car: 0.6103
  • Map Other: 0.0
  • Mar 100 Other: 0.0
  • Map Pickup: 0.0088
  • Mar 100 Pickup: 0.2
  • Map Truck: 0.0
  • Mar 100 Truck: 0.0
  • Map Van: 0.0
  • Mar 100 Van: 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: 8
  • 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

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 Camping car Mar 100 Camping car Map Car Mar 100 Car Map Other Mar 100 Other Map Pickup Mar 100 Pickup Map Truck Mar 100 Truck Map Van Mar 100 Van
No log 1.0 232 1.2888 0.0179 0.0435 0.0095 0.118 0.0187 0.0 0.0175 0.0527 0.0838 0.4665 0.0955 0.0 0.0 0.0 0.1073 0.5028 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
No log 2.0 464 1.1028 0.0308 0.0792 0.0172 0.2225 0.0267 0.0 0.0233 0.0637 0.0818 0.4346 0.0998 0.0 0.0 0.0 0.1851 0.4907 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.5836 3.0 696 1.0727 0.0509 0.1216 0.0268 0.3118 0.0531 0.0 0.0278 0.0702 0.0781 0.4362 0.0884 0.0 0.0 0.0 0.3055 0.4683 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.5836 4.0 928 1.1038 0.0467 0.1199 0.0193 0.2617 0.0543 0.0 0.0249 0.0679 0.0772 0.4357 0.0861 0.0 0.0 0.0 0.2803 0.4633 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.1611 5.0 1160 1.0186 0.057 0.13 0.0335 0.3254 0.0643 0.0 0.0281 0.0721 0.0819 0.4735 0.0877 0.0 0.0 0.0 0.3422 0.4915 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.1611 6.0 1392 1.0468 0.0541 0.1255 0.029 0.3094 0.0612 0.0 0.0288 0.0735 0.0816 0.4557 0.0925 0.0 0.0 0.0 0.3249 0.4897 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.1009 7.0 1624 0.9479 0.063 0.1366 0.0467 0.3682 0.0676 0.0 0.0312 0.0789 0.0905 0.507 0.1019 0.0 0.0 0.0 0.3781 0.5427 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.1009 8.0 1856 0.9808 0.0556 0.1305 0.0355 0.3103 0.0652 0.0 0.0295 0.074 0.0858 0.4789 0.0974 0.0 0.0 0.0 0.3338 0.5149 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.043 9.0 2088 1.0168 0.0504 0.1232 0.0248 0.2652 0.0654 0.0 0.0271 0.0716 0.0815 0.4443 0.0958 0.0 0.0 0.0 0.3022 0.489 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.043 10.0 2320 0.9497 0.0536 0.1207 0.0312 0.2924 0.0715 0.0 0.0299 0.0755 0.0888 0.4968 0.1003 0.0 0.0 0.0 0.3218 0.5327 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.026 11.0 2552 0.9826 0.0844 0.1826 0.0374 0.3582 0.0936 0.0 0.0727 0.1434 0.1534 0.4795 0.1667 0.0 0.127 0.4 0.3792 0.5206 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.026 12.0 2784 0.9282 0.0615 0.1396 0.0422 0.3292 0.0758 0.0 0.0809 0.1281 0.1357 0.4784 0.1472 0.0 0.0202 0.3 0.3487 0.5142 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.9794 13.0 3016 0.8976 0.0663 0.1368 0.0541 0.3854 0.0748 0.0 0.0317 0.0821 0.0942 0.5389 0.1026 0.0 0.0 0.0 0.3977 0.5651 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.9794 14.0 3248 0.8970 0.0633 0.1358 0.0474 0.3519 0.0754 0.0 0.0315 0.0799 0.0929 0.5151 0.1066 0.0 0.0 0.0 0.3798 0.5577 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.9794 15.0 3480 0.8418 0.0677 0.1368 0.052 0.3737 0.0807 0.0 0.0328 0.0837 0.0935 0.5265 0.1045 0.0 0.0 0.0 0.4063 0.5609 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.9189 16.0 3712 0.8709 0.0677 0.1414 0.055 0.3733 0.0808 0.0 0.066 0.1151 0.1261 0.5308 0.1344 0.0 0.0033 0.2 0.4031 0.5566 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.9189 17.0 3944 0.9095 0.0657 0.1386 0.048 0.3734 0.0746 0.0 0.0444 0.0925 0.1004 0.4935 0.1139 0.0 0.0 0.0 0.3918 0.5356 0.0 0.0 0.0024 0.0667 0.0 0.0 0.0 0.0
0.8933 18.0 4176 0.8832 0.0651 0.1416 0.046 0.3467 0.0809 0.0 0.1021 0.1488 0.1594 0.5076 0.1698 0.0 0.0151 0.3 0.3708 0.5399 0.0 0.0 0.0045 0.1167 0.0 0.0 0.0 0.0
0.8933 19.0 4408 0.8459 0.0707 0.1444 0.0527 0.4093 0.0782 0.0 0.043 0.0914 0.1017 0.5405 0.1082 0.0 0.0 0.0 0.4231 0.5605 0.0 0.0 0.0013 0.05 0.0 0.0 0.0 0.0
0.8732 20.0 4640 0.8352 0.0719 0.151 0.0581 0.3987 0.0853 0.0 0.0699 0.1257 0.1366 0.5459 0.1443 0.0 0.001 0.05 0.4198 0.5698 0.0 0.0 0.0106 0.2 0.0 0.0 0.0 0.0
0.8732 21.0 4872 0.8110 0.077 0.1574 0.0657 0.407 0.0938 0.0 0.1345 0.1896 0.2005 0.5638 0.2078 0.0 0.0104 0.3 0.4317 0.5865 0.0 0.0 0.0201 0.3167 0.0 0.0 0.0 0.0
0.8261 22.0 5104 0.8235 0.074 0.1564 0.0561 0.4045 0.0867 0.0 0.0842 0.1682 0.1776 0.5422 0.1851 0.0 0.0066 0.15 0.4229 0.5655 0.0 0.0 0.0144 0.35 0.0 0.0 0.0 0.0
0.8261 23.0 5336 0.8116 0.0817 0.1636 0.0731 0.4237 0.0965 0.0 0.1115 0.1724 0.1822 0.5557 0.1889 0.0 0.0433 0.3 0.4392 0.5765 0.0 0.0 0.0074 0.2167 0.0 0.0 0.0 0.0
0.8213 24.0 5568 0.7748 0.0841 0.1619 0.0746 0.4393 0.1182 0.0 0.1244 0.1874 0.197 0.58 0.2031 0.0 0.0393 0.35 0.457 0.5989 0.0 0.0 0.0085 0.2333 0.0 0.0 0.0 0.0
0.8213 25.0 5800 0.7782 0.0893 0.1678 0.0802 0.4418 0.1555 0.0 0.1376 0.2022 0.2134 0.5795 0.2191 0.0 0.0683 0.45 0.4583 0.5972 0.0 0.0 0.009 0.2333 0.0 0.0 0.0 0.0
0.794 26.0 6032 0.7822 0.082 0.1585 0.0738 0.4368 0.1026 0.0 0.118 0.191 0.2021 0.5773 0.208 0.0 0.0324 0.4 0.4519 0.5957 0.0 0.0 0.0074 0.2167 0.0 0.0 0.0 0.0
0.794 27.0 6264 0.7735 0.0888 0.1683 0.0879 0.4533 0.1188 0.0 0.1404 0.1981 0.2087 0.5865 0.2137 0.0 0.0505 0.35 0.4672 0.6021 0.0 0.0 0.0153 0.3 0.0 0.0 0.0 0.0
0.794 28.0 6496 0.7680 0.0852 0.1608 0.0842 0.4557 0.109 0.0 0.1186 0.1821 0.1924 0.5892 0.1974 0.0 0.0321 0.35 0.4705 0.6046 0.0 0.0 0.0088 0.2 0.0 0.0 0.0 0.0
0.7843 29.0 6728 0.7678 0.0852 0.1614 0.0817 0.4516 0.1095 0.0 0.1185 0.1821 0.1931 0.593 0.1981 0.0 0.0353 0.35 0.4673 0.6085 0.0 0.0 0.0088 0.2 0.0 0.0 0.0 0.0
0.7843 30.0 6960 0.7673 0.0853 0.1613 0.0814 0.452 0.1098 0.0 0.1187 0.1824 0.1934 0.5946 0.1984 0.0 0.0353 0.35 0.4678 0.6103 0.0 0.0 0.0088 0.2 0.0 0.0 0.0 0.0

Framework versions

  • Transformers 4.42.4
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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