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drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_freeze_probs

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dinov2
multilabel-image-classification
multilabel
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Evaluation on the test set completed on 2024_11_14.
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metadata
license: apache-2.0
base_model: facebook/dinov2-large
tags:
  - generated_from_trainer
model-index:
  - name: drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_freeze_probs
    results: []

drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_freeze_probs

This model is a fine-tuned version of facebook/dinov2-large on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4061
  • Rmse: 0.2019
  • Mae: 0.1446
  • Kl Divergence: 0.9802
  • Explained Variance: 0.3860
  • Learning Rate: 0.0000

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: 0.001
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 150
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rmse Mae Kl Divergence Explained Variance Rate
No log 1.0 438 0.4306 0.2210 0.1621 1.0069 0.2882 0.001
0.4808 2.0 876 0.4246 0.2179 0.1547 1.3119 0.3118 0.001
0.421 3.0 1314 0.4223 0.2158 0.1554 1.0982 0.3192 0.001
0.4151 4.0 1752 0.4191 0.2142 0.1552 1.0414 0.3351 0.001
0.4114 5.0 2190 0.4171 0.2123 0.1541 1.0698 0.3384 0.001
0.4089 6.0 2628 0.4209 0.2140 0.1520 1.1959 0.3311 0.001
0.4091 7.0 3066 0.4166 0.2126 0.1530 1.1709 0.3382 0.001
0.4071 8.0 3504 0.4195 0.2143 0.1556 0.9712 0.3346 0.001
0.4071 9.0 3942 0.4167 0.2121 0.1524 1.1432 0.3415 0.001
0.4062 10.0 4380 0.4186 0.2139 0.1535 0.9121 0.3420 0.001
0.4052 11.0 4818 0.4156 0.2114 0.1536 0.9950 0.3442 0.001
0.406 12.0 5256 0.4188 0.2139 0.1555 1.0106 0.3390 0.001
0.4058 13.0 5694 0.4163 0.2121 0.1553 1.1482 0.3425 0.001
0.4056 14.0 6132 0.4193 0.2138 0.1546 1.2111 0.3286 0.001
0.4033 15.0 6570 0.4162 0.2121 0.1542 1.2043 0.3402 0.001
0.4057 16.0 7008 0.4139 0.2102 0.1528 1.0828 0.3500 0.001
0.4057 17.0 7446 0.4171 0.2118 0.1564 1.0006 0.3430 0.001
0.405 18.0 7884 0.4146 0.2107 0.1507 1.0514 0.3499 0.001
0.4035 19.0 8322 0.4186 0.2114 0.1532 0.9575 0.3468 0.001
0.4031 20.0 8760 0.4143 0.2108 0.1513 1.1648 0.3487 0.001
0.4048 21.0 9198 0.4195 0.2123 0.1533 1.2950 0.3385 0.001
0.4055 22.0 9636 0.4340 0.2110 0.1524 inf 0.3463 0.001
0.4022 23.0 10074 0.4327 0.2085 0.1517 nan 0.3621 0.0001
0.3978 24.0 10512 0.4385 0.2092 0.1493 nan 0.3583 0.0001
0.3978 25.0 10950 0.4272 0.2074 0.1490 inf 0.3649 0.0001
0.3988 26.0 11388 0.4105 0.2075 0.1480 1.1903 0.3644 0.0001
0.3958 27.0 11826 0.4096 0.2067 0.1494 0.9915 0.3688 0.0001
0.3965 28.0 12264 0.4104 0.2075 0.1493 0.9669 0.3681 0.0001
0.396 29.0 12702 0.4097 0.2069 0.1469 1.0433 0.3696 0.0001
0.3936 30.0 13140 0.4094 0.2065 0.1490 0.9082 0.3731 0.0001
0.3944 31.0 13578 0.4091 0.2065 0.1470 1.0120 0.3705 0.0001
0.3941 32.0 14016 0.4084 0.2060 0.1483 0.9708 0.3742 0.0001
0.3941 33.0 14454 0.4082 0.2057 0.1474 0.9317 0.3755 0.0001
0.3933 34.0 14892 0.4085 0.2061 0.1481 0.9619 0.3747 0.0001
0.3926 35.0 15330 0.4073 0.2054 0.1466 1.0523 0.3758 0.0001
0.3936 36.0 15768 0.4074 0.2052 0.1460 1.0622 0.3771 0.0001
0.3935 37.0 16206 0.4066 0.2047 0.1456 1.0201 0.3802 0.0001
0.3927 38.0 16644 0.4064 0.2045 0.1459 1.0557 0.3800 0.0001
0.392 39.0 17082 0.4078 0.2056 0.1469 1.0055 0.3771 0.0001
0.3915 40.0 17520 0.4068 0.2049 0.1464 0.9849 0.3805 0.0001
0.3915 41.0 17958 0.4089 0.2063 0.1489 0.8999 0.3778 0.0001
0.3907 42.0 18396 0.4069 0.2049 0.1463 1.0617 0.3797 0.0001
0.3919 43.0 18834 0.4058 0.2041 0.1450 1.0520 0.3830 0.0001
0.3902 44.0 19272 0.4071 0.2050 0.1475 1.0054 0.3809 0.0001
0.3896 45.0 19710 0.4067 0.2047 0.1440 1.1386 0.3813 0.0001
0.3925 46.0 20148 0.4067 0.2047 0.1457 1.0253 0.3831 0.0001
0.3896 47.0 20586 0.4062 0.2043 0.1473 1.0430 0.3834 0.0001
0.3902 48.0 21024 0.4065 0.2048 0.1457 1.1041 0.3812 0.0001
0.3902 49.0 21462 0.4071 0.2052 0.1463 1.0702 0.3798 0.0001
0.3897 50.0 21900 0.4064 0.2042 0.1479 0.8917 0.3857 1e-05
0.3875 51.0 22338 0.4058 0.2041 0.1437 0.9960 0.3845 1e-05
0.3874 52.0 22776 0.4053 0.2037 0.1446 1.0567 0.3851 1e-05
0.3899 53.0 23214 0.4056 0.2039 0.1462 1.0205 0.3859 1e-05
0.3892 54.0 23652 0.4059 0.2041 0.1441 0.9905 0.3854 1e-05
0.3892 55.0 24090 0.4061 0.2041 0.1471 0.9379 0.3856 1e-05
0.3869 56.0 24528 0.4059 0.2041 0.1454 0.9696 0.3854 1e-05
0.3869 57.0 24966 0.4058 0.2041 0.1460 1.0591 0.3842 1e-05
0.3874 58.0 25404 0.4063 0.2043 0.1460 0.9276 0.3860 1e-05
0.3887 59.0 25842 0.4056 0.2038 0.1453 0.9794 0.3868 0.0000
0.3882 60.0 26280 0.4057 0.2040 0.1446 1.0349 0.3851 0.0000
0.389 61.0 26718 0.4058 0.2041 0.1449 0.9860 0.3857 0.0000
0.3882 62.0 27156 0.4054 0.2037 0.1446 0.9528 0.3865 0.0000

Framework versions

  • Transformers 4.41.0
  • Pytorch 2.5.0+cu124
  • Datasets 3.0.2
  • Tokenizers 0.19.1