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# Amoros_Beaugosse_test-large-2025_05_20_57422-bs16_freeze

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: 0.0987
- F1 Micro: 0.5482
- F1 Macro: 0.3454
- Accuracy: 0.4313
- Learning Rate: 1e-05

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Accuracy | Rate   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:------:|
| No log        | 1.0   | 195  | 0.8014          | 0.1708   | 0.1167   | 0.1568   | 0.001  |
| No log        | 2.0   | 390  | 0.1238          | 0.3956   | 0.1496   | 0.2677   | 0.001  |
| 0.2531        | 3.0   | 585  | 0.1158          | 0.4517   | 0.1890   | 0.3270   | 0.001  |
| 0.2531        | 4.0   | 780  | 0.1146          | 0.4248   | 0.1657   | 0.2945   | 0.001  |
| 0.2531        | 5.0   | 975  | 0.1105          | 0.4866   | 0.2335   | 0.3652   | 0.001  |
| 0.1272        | 6.0   | 1170 | 0.1105          | 0.4681   | 0.2026   | 0.3442   | 0.001  |
| 0.1272        | 7.0   | 1365 | 0.1122          | 0.4576   | 0.2113   | 0.3308   | 0.001  |
| 0.1158        | 8.0   | 1560 | 0.1062          | 0.4813   | 0.2474   | 0.3461   | 0.001  |
| 0.1158        | 9.0   | 1755 | 0.1058          | 0.4863   | 0.2428   | 0.3537   | 0.001  |
| 0.1158        | 10.0  | 1950 | 0.1075          | 0.4813   | 0.2409   | 0.3480   | 0.001  |
| 0.1126        | 11.0  | 2145 | 0.1058          | 0.4942   | 0.2747   | 0.3595   | 0.001  |
| 0.1126        | 12.0  | 2340 | 0.1065          | 0.4944   | 0.2533   | 0.3738   | 0.001  |
| 0.1098        | 13.0  | 2535 | 0.1071          | 0.4823   | 0.2584   | 0.3614   | 0.001  |
| 0.1098        | 14.0  | 2730 | 0.1086          | 0.5022   | 0.2807   | 0.3776   | 0.001  |
| 0.1098        | 15.0  | 2925 | 0.1056          | 0.5032   | 0.2649   | 0.3767   | 0.001  |
| 0.108         | 16.0  | 3120 | 0.1077          | 0.5106   | 0.2838   | 0.3958   | 0.001  |
| 0.108         | 17.0  | 3315 | 0.1072          | 0.4892   | 0.2847   | 0.3709   | 0.001  |
| 0.1064        | 18.0  | 3510 | 0.1057          | 0.4962   | 0.2752   | 0.3709   | 0.001  |
| 0.1064        | 19.0  | 3705 | 0.1053          | 0.4917   | 0.2690   | 0.3671   | 0.001  |
| 0.1064        | 20.0  | 3900 | 0.1052          | 0.5016   | 0.2853   | 0.3738   | 0.001  |
| 0.1047        | 21.0  | 4095 | 0.1064          | 0.5085   | 0.2684   | 0.3728   | 0.001  |
| 0.1047        | 22.0  | 4290 | 0.1057          | 0.5067   | 0.3060   | 0.3767   | 0.001  |
| 0.1047        | 23.0  | 4485 | 0.1059          | 0.5094   | 0.3023   | 0.3862   | 0.001  |
| 0.1049        | 24.0  | 4680 | 0.1055          | 0.5167   | 0.3091   | 0.3910   | 0.001  |
| 0.1049        | 25.0  | 4875 | 0.1059          | 0.4856   | 0.3083   | 0.3614   | 0.001  |
| 0.1037        | 26.0  | 5070 | 0.1073          | 0.4931   | 0.2853   | 0.3738   | 0.001  |
| 0.1037        | 27.0  | 5265 | 0.1006          | 0.5214   | 0.3116   | 0.3948   | 0.0001 |
| 0.1037        | 28.0  | 5460 | 0.1003          | 0.5369   | 0.3350   | 0.4092   | 0.0001 |
| 0.0968        | 29.0  | 5655 | 0.1000          | 0.5425   | 0.3217   | 0.4197   | 0.0001 |
| 0.0968        | 30.0  | 5850 | 0.0993          | 0.5555   | 0.3540   | 0.4331   | 0.0001 |
| 0.0932        | 31.0  | 6045 | 0.0993          | 0.5562   | 0.3749   | 0.4359   | 0.0001 |
| 0.0932        | 32.0  | 6240 | 0.0990          | 0.5597   | 0.3724   | 0.4369   | 0.0001 |
| 0.0932        | 33.0  | 6435 | 0.0991          | 0.5521   | 0.3543   | 0.4293   | 0.0001 |
| 0.0899        | 34.0  | 6630 | 0.0986          | 0.5636   | 0.3742   | 0.4446   | 0.0001 |
| 0.0899        | 35.0  | 6825 | 0.0990          | 0.5634   | 0.3743   | 0.4455   | 0.0001 |
| 0.0882        | 36.0  | 7020 | 0.0997          | 0.5573   | 0.3655   | 0.4407   | 0.0001 |
| 0.0882        | 37.0  | 7215 | 0.1018          | 0.5635   | 0.3725   | 0.4493   | 0.0001 |
| 0.0882        | 38.0  | 7410 | 0.0988          | 0.5694   | 0.3782   | 0.4541   | 0.0001 |
| 0.0846        | 39.0  | 7605 | 0.0991          | 0.5684   | 0.3795   | 0.4541   | 0.0001 |
| 0.0846        | 40.0  | 7800 | 0.0994          | 0.5692   | 0.3688   | 0.4570   | 0.0001 |
| 0.0846        | 41.0  | 7995 | 0.0995          | 0.5711   | 0.3758   | 0.4551   | 1e-05  |
| 0.0832        | 42.0  | 8190 | 0.0991          | 0.5714   | 0.3770   | 0.4608   | 1e-05  |
| 0.0832        | 43.0  | 8385 | 0.0993          | 0.5753   | 0.3807   | 0.4598   | 1e-05  |
| 0.0824        | 44.0  | 8580 | 0.0988          | 0.5640   | 0.3753   | 0.4503   | 1e-05  |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.6.0+cu118
- Datasets 3.0.2
- Tokenizers 0.21.1

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