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---
license: apache-2.0
base_model: facebook/dinov2-large
tags:
- generated_from_trainer
model-index:
- name: drone-DinoVdeau-from-probs-large-2024_11_14-batch-size16_freeze_probs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# drone-DinoVdeau-from-probs-large-2024_11_14-batch-size16_freeze_probs
This model is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4672
- Rmse: 0.1550
- Mae: 0.1155
- Kl Divergence: 0.3295
- Explained Variance: 0.4649
- 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 | 0.4934 | 0.1825 | 0.1294 | 0.9903 | 0.3297 | 0.001 |
| 0.5313 | 2.0 | 876 | 0.4789 | 0.1716 | 0.1262 | 0.6847 | 0.3731 | 0.001 |
| 0.4831 | 3.0 | 1314 | 0.4788 | 0.1709 | 0.1271 | 0.5498 | 0.3824 | 0.001 |
| 0.4773 | 4.0 | 1752 | 0.4766 | 0.1695 | 0.1278 | 0.3131 | 0.3979 | 0.001 |
| 0.476 | 5.0 | 2190 | 0.4765 | 0.1687 | 0.1277 | 0.4013 | 0.3970 | 0.001 |
| 0.4746 | 6.0 | 2628 | 0.4765 | 0.1689 | 0.1243 | 0.6370 | 0.3924 | 0.001 |
| 0.4738 | 7.0 | 3066 | 0.4763 | 0.1694 | 0.1292 | 0.4314 | 0.3911 | 0.001 |
| 0.4727 | 8.0 | 3504 | 0.4755 | 0.1681 | 0.1267 | 0.3379 | 0.4076 | 0.001 |
| 0.4727 | 9.0 | 3942 | 0.4734 | 0.1662 | 0.1250 | 0.4916 | 0.4072 | 0.001 |
| 0.4715 | 10.0 | 4380 | 0.4755 | 0.1677 | 0.1277 | 0.3348 | 0.4062 | 0.001 |
| 0.4714 | 11.0 | 4818 | 0.4731 | 0.1659 | 0.1255 | 0.3524 | 0.4154 | 0.001 |
| 0.4713 | 12.0 | 5256 | 0.4768 | 0.1690 | 0.1306 | 0.2383 | 0.4103 | 0.001 |
| 0.4722 | 13.0 | 5694 | 0.4737 | 0.1666 | 0.1223 | 0.6968 | 0.4028 | 0.001 |
| 0.472 | 14.0 | 6132 | 0.4737 | 0.1658 | 0.1254 | 0.3983 | 0.4099 | 0.001 |
| 0.4697 | 15.0 | 6570 | 0.4739 | 0.1664 | 0.1248 | 0.5620 | 0.4036 | 0.001 |
| 0.4721 | 16.0 | 7008 | 0.4720 | 0.1648 | 0.1231 | 0.6049 | 0.4159 | 0.001 |
| 0.4721 | 17.0 | 7446 | 0.4741 | 0.1664 | 0.1265 | 0.3072 | 0.4171 | 0.001 |
| 0.4709 | 18.0 | 7884 | 0.4738 | 0.1650 | 0.1253 | 0.3350 | 0.4239 | 0.001 |
| 0.4711 | 19.0 | 8322 | 0.4763 | 0.1672 | 0.1282 | 0.2746 | 0.4162 | 0.001 |
| 0.4696 | 20.0 | 8760 | 0.4756 | 0.1670 | 0.1245 | 0.5659 | 0.4060 | 0.001 |
| 0.4715 | 21.0 | 9198 | 0.4734 | 0.1662 | 0.1230 | 0.6154 | 0.4061 | 0.001 |
| 0.4714 | 22.0 | 9636 | 0.4744 | 0.1677 | 0.1223 | 0.7974 | 0.4027 | 0.001 |
| 0.4697 | 23.0 | 10074 | 0.4721 | 0.1639 | 0.1252 | 0.2307 | 0.4337 | 0.0001 |
| 0.4653 | 24.0 | 10512 | 0.4706 | 0.1631 | 0.1217 | 0.4219 | 0.4314 | 0.0001 |
| 0.4653 | 25.0 | 10950 | 0.4688 | 0.1612 | 0.1195 | 0.5242 | 0.4371 | 0.0001 |
| 0.4665 | 26.0 | 11388 | 0.4693 | 0.1620 | 0.1190 | 0.6159 | 0.4338 | 0.0001 |
| 0.4638 | 27.0 | 11826 | 0.4685 | 0.1607 | 0.1206 | 0.4046 | 0.4416 | 0.0001 |
| 0.4647 | 28.0 | 12264 | 0.4694 | 0.1616 | 0.1220 | 0.2860 | 0.4443 | 0.0001 |
| 0.4644 | 29.0 | 12702 | 0.4689 | 0.1614 | 0.1197 | 0.4270 | 0.4401 | 0.0001 |
| 0.4638 | 30.0 | 13140 | 0.4699 | 0.1619 | 0.1225 | 0.2625 | 0.4436 | 0.0001 |
| 0.4636 | 31.0 | 13578 | 0.4684 | 0.1607 | 0.1197 | 0.3876 | 0.4431 | 0.0001 |
| 0.463 | 32.0 | 14016 | 0.4678 | 0.1600 | 0.1195 | 0.4060 | 0.4467 | 0.0001 |
| 0.463 | 33.0 | 14454 | 0.4676 | 0.1596 | 0.1193 | 0.3688 | 0.4494 | 0.0001 |
| 0.4628 | 34.0 | 14892 | 0.4677 | 0.1600 | 0.1194 | 0.3900 | 0.4491 | 0.0001 |
| 0.4616 | 35.0 | 15330 | 0.4670 | 0.1593 | 0.1189 | 0.4282 | 0.4500 | 0.0001 |
| 0.4634 | 36.0 | 15768 | 0.4668 | 0.1591 | 0.1180 | 0.4446 | 0.4506 | 0.0001 |
| 0.462 | 37.0 | 16206 | 0.4669 | 0.1590 | 0.1185 | 0.3942 | 0.4528 | 0.0001 |
| 0.4631 | 38.0 | 16644 | 0.4665 | 0.1588 | 0.1177 | 0.4783 | 0.4512 | 0.0001 |
| 0.4603 | 39.0 | 17082 | 0.4674 | 0.1597 | 0.1190 | 0.3868 | 0.4500 | 0.0001 |
| 0.4614 | 40.0 | 17520 | 0.4677 | 0.1599 | 0.1195 | 0.3627 | 0.4498 | 0.0001 |
| 0.4614 | 41.0 | 17958 | 0.4682 | 0.1602 | 0.1211 | 0.2655 | 0.4540 | 0.0001 |
| 0.4612 | 42.0 | 18396 | 0.4665 | 0.1589 | 0.1172 | 0.5072 | 0.4514 | 0.0001 |
| 0.462 | 43.0 | 18834 | 0.4664 | 0.1585 | 0.1177 | 0.4306 | 0.4555 | 0.0001 |
| 0.4603 | 44.0 | 19272 | 0.4671 | 0.1594 | 0.1192 | 0.4115 | 0.4529 | 0.0001 |
| 0.4599 | 45.0 | 19710 | 0.4666 | 0.1590 | 0.1171 | 0.4417 | 0.4528 | 0.0001 |
| 0.4612 | 46.0 | 20148 | 0.4663 | 0.1585 | 0.1179 | 0.3686 | 0.4574 | 0.0001 |
| 0.4596 | 47.0 | 20586 | 0.4658 | 0.1582 | 0.1172 | 0.5090 | 0.4567 | 0.0001 |
| 0.4603 | 48.0 | 21024 | 0.4663 | 0.1589 | 0.1175 | 0.5279 | 0.4548 | 0.0001 |
| 0.4603 | 49.0 | 21462 | 0.4666 | 0.1591 | 0.1183 | 0.4497 | 0.4532 | 0.0001 |
| 0.4599 | 50.0 | 21900 | 0.4676 | 0.1595 | 0.1205 | 0.2712 | 0.4580 | 0.0001 |
| 0.4594 | 51.0 | 22338 | 0.4664 | 0.1586 | 0.1172 | 0.4008 | 0.4552 | 0.0001 |
| 0.4593 | 52.0 | 22776 | 0.4659 | 0.1583 | 0.1163 | 0.4922 | 0.4557 | 0.0001 |
| 0.4614 | 53.0 | 23214 | 0.4657 | 0.1579 | 0.1178 | 0.4274 | 0.4601 | 0.0001 |
| 0.4592 | 54.0 | 23652 | 0.4663 | 0.1585 | 0.1158 | 0.4574 | 0.4570 | 0.0001 |
| 0.4601 | 55.0 | 24090 | 0.4664 | 0.1586 | 0.1189 | 0.3486 | 0.4580 | 0.0001 |
| 0.4589 | 56.0 | 24528 | 0.4662 | 0.1584 | 0.1184 | 0.3016 | 0.4612 | 0.0001 |
| 0.4589 | 57.0 | 24966 | 0.4663 | 0.1587 | 0.1181 | 0.4163 | 0.4553 | 0.0001 |
| 0.4588 | 58.0 | 25404 | 0.4674 | 0.1593 | 0.1189 | 0.3399 | 0.4557 | 0.0001 |
| 0.4595 | 59.0 | 25842 | 0.4650 | 0.1572 | 0.1170 | 0.3686 | 0.4646 | 0.0001 |
| 0.4594 | 60.0 | 26280 | 0.4660 | 0.1584 | 0.1172 | 0.4535 | 0.4567 | 0.0001 |
| 0.4599 | 61.0 | 26718 | 0.4662 | 0.1585 | 0.1179 | 0.3751 | 0.4584 | 0.0001 |
| 0.4584 | 62.0 | 27156 | 0.4661 | 0.1583 | 0.1173 | 0.3534 | 0.4588 | 0.0001 |
| 0.4575 | 63.0 | 27594 | 0.4660 | 0.1583 | 0.1163 | 0.4048 | 0.4577 | 0.0001 |
| 0.4598 | 64.0 | 28032 | 0.4671 | 0.1588 | 0.1188 | 0.2471 | 0.4629 | 0.0001 |
| 0.4598 | 65.0 | 28470 | 0.4654 | 0.1577 | 0.1166 | 0.4526 | 0.4604 | 0.0001 |
| 0.4582 | 66.0 | 28908 | 0.4657 | 0.1582 | 0.1161 | 0.5259 | 0.4592 | 1e-05 |
| 0.4592 | 67.0 | 29346 | 0.4654 | 0.1574 | 0.1173 | 0.4252 | 0.4623 | 1e-05 |
| 0.4573 | 68.0 | 29784 | 0.4649 | 0.1572 | 0.1154 | 0.4989 | 0.4614 | 1e-05 |
| 0.4576 | 69.0 | 30222 | 0.4651 | 0.1570 | 0.1161 | 0.4023 | 0.4644 | 1e-05 |
| 0.4556 | 70.0 | 30660 | 0.4660 | 0.1576 | 0.1166 | 0.4118 | 0.4622 | 1e-05 |
| 0.4591 | 71.0 | 31098 | 0.4661 | 0.1578 | 0.1177 | 0.3075 | 0.4644 | 1e-05 |
| 0.4563 | 72.0 | 31536 | 0.4658 | 0.1580 | 0.1171 | 0.3836 | 0.4621 | 1e-05 |
| 0.4563 | 73.0 | 31974 | 0.4649 | 0.1569 | 0.1154 | 0.4544 | 0.4640 | 1e-05 |
| 0.4577 | 74.0 | 32412 | 0.4647 | 0.1567 | 0.1163 | 0.4538 | 0.4660 | 1e-05 |
| 0.4576 | 75.0 | 32850 | 0.4656 | 0.1573 | 0.1166 | 0.3348 | 0.4658 | 1e-05 |
| 0.457 | 76.0 | 33288 | 0.4647 | 0.1571 | 0.1158 | 0.4976 | 0.4645 | 1e-05 |
| 0.4574 | 77.0 | 33726 | 0.4651 | 0.1570 | 0.1163 | 0.3934 | 0.4653 | 1e-05 |
| 0.457 | 78.0 | 34164 | 0.4650 | 0.1571 | 0.1161 | 0.3936 | 0.4654 | 1e-05 |
| 0.4566 | 79.0 | 34602 | 0.4653 | 0.1573 | 0.1159 | 0.3759 | 0.4653 | 1e-05 |
| 0.458 | 80.0 | 35040 | 0.4647 | 0.1567 | 0.1162 | 0.4189 | 0.4660 | 1e-05 |
| 0.458 | 81.0 | 35478 | 0.4649 | 0.1571 | 0.1158 | 0.4751 | 0.4647 | 0.0000 |
| 0.456 | 82.0 | 35916 | 0.4654 | 0.1572 | 0.1161 | 0.4335 | 0.4651 | 0.0000 |
| 0.4564 | 83.0 | 36354 | 0.4647 | 0.1566 | 0.1161 | 0.3906 | 0.4667 | 0.0000 |
| 0.4575 | 84.0 | 36792 | 0.4643 | 0.1564 | 0.1157 | 0.3855 | 0.4677 | 0.0000 |
| 0.4557 | 85.0 | 37230 | 0.4653 | 0.1571 | 0.1173 | 0.3372 | 0.4669 | 0.0000 |
| 0.4587 | 86.0 | 37668 | 0.4655 | 0.1572 | 0.1184 | 0.2969 | 0.4686 | 0.0000 |
| 0.4564 | 87.0 | 38106 | 0.4652 | 0.1571 | 0.1173 | 0.3572 | 0.4670 | 0.0000 |
| 0.4565 | 88.0 | 38544 | 0.4656 | 0.1578 | 0.1151 | 0.5179 | 0.4627 | 0.0000 |
| 0.4565 | 89.0 | 38982 | 0.4654 | 0.1574 | 0.1177 | 0.2948 | 0.4670 | 0.0000 |
| 0.4569 | 90.0 | 39420 | 0.4650 | 0.1569 | 0.1167 | 0.3427 | 0.4674 | 0.0000 |
| 0.4561 | 91.0 | 39858 | 0.4655 | 0.1572 | 0.1173 | 0.2790 | 0.4691 | 0.0000 |
| 0.4575 | 92.0 | 40296 | 0.4646 | 0.1566 | 0.1153 | 0.4153 | 0.4672 | 0.0000 |
| 0.4569 | 93.0 | 40734 | 0.4649 | 0.1571 | 0.1153 | 0.4664 | 0.4645 | 0.0000 |
| 0.456 | 94.0 | 41172 | 0.4653 | 0.1568 | 0.1159 | 0.3859 | 0.4662 | 0.0000 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.2
- Tokenizers 0.19.1
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