transformers tensorboard safetensors dinov2 multilabel-image-classification multilabel generated_from_trainer eng base_model:facebook/dinov2-large license:apache-2.0 endpoints_compatible region:us
---
language:
- eng
license: apache-2.0
base_model: facebook/dinov2-large
tags:
- multilabel-image-classification
- multilabel
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dinov2-large-2024_01_19-with_data_aug_batch-size32_epochs50_freeze
  results: []
---

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# dinov2-large-2024_01_19-with_data_aug_batch-size32_epochs50_freeze

This model is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the multilabel_complete_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0893
- F1 Micro: 0.8609
- F1 Macro: 0.8255
- Roc Auc: 0.9106
- Accuracy: 0.5640
- Learning Rate: 0.0001

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1 Micro | F1 Macro | Roc Auc | Accuracy | Rate   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-------:|:--------:|:------:|
| No log        | 1.0   | 274   | 0.1433          | 0.7651   | 0.6114   | 0.8511  | 0.4395   | 0.001  |
| 0.2465        | 2.0   | 548   | 0.1238          | 0.8065   | 0.7226   | 0.8761  | 0.4896   | 0.001  |
| 0.2465        | 3.0   | 822   | 0.1152          | 0.8172   | 0.7453   | 0.8789  | 0.5191   | 0.001  |
| 0.1402        | 4.0   | 1096  | 0.1138          | 0.8186   | 0.7360   | 0.8777  | 0.5184   | 0.001  |
| 0.1402        | 5.0   | 1370  | 0.1144          | 0.8230   | 0.7547   | 0.8817  | 0.5184   | 0.001  |
| 0.1317        | 6.0   | 1644  | 0.1100          | 0.8201   | 0.7469   | 0.8745  | 0.5261   | 0.001  |
| 0.1317        | 7.0   | 1918  | 0.1125          | 0.8241   | 0.7511   | 0.8797  | 0.5306   | 0.001  |
| 0.1284        | 8.0   | 2192  | 0.1084          | 0.8320   | 0.7709   | 0.8895  | 0.5411   | 0.001  |
| 0.1284        | 9.0   | 2466  | 0.1115          | 0.8266   | 0.7647   | 0.8936  | 0.5198   | 0.001  |
| 0.1267        | 10.0  | 2740  | 0.1100          | 0.8328   | 0.7685   | 0.8935  | 0.5334   | 0.001  |
| 0.1251        | 11.0  | 3014  | 0.1750          | 0.8081   | 0.7345   | 0.8760  | 0.5177   | 0.001  |
| 0.1251        | 12.0  | 3288  | 0.1086          | 0.8244   | 0.7612   | 0.8779  | 0.5379   | 0.001  |
| 0.1247        | 13.0  | 3562  | 0.1064          | 0.8295   | 0.7613   | 0.8870  | 0.5320   | 0.001  |
| 0.1247        | 14.0  | 3836  | 0.1050          | 0.8318   | 0.7684   | 0.8886  | 0.5289   | 0.001  |
| 0.123         | 15.0  | 4110  | 0.1043          | 0.8362   | 0.7696   | 0.8915  | 0.5341   | 0.001  |
| 0.123         | 16.0  | 4384  | 0.1045          | 0.8428   | 0.7891   | 0.9073  | 0.5341   | 0.001  |
| 0.1229        | 17.0  | 4658  | 0.1059          | 0.8386   | 0.7775   | 0.9033  | 0.5143   | 0.001  |
| 0.1229        | 18.0  | 4932  | 0.1063          | 0.8308   | 0.7606   | 0.8906  | 0.5261   | 0.001  |
| 0.1205        | 19.0  | 5206  | 0.1046          | 0.8367   | 0.7733   | 0.8916  | 0.5421   | 0.001  |
| 0.1205        | 20.0  | 5480  | 0.1091          | 0.8384   | 0.7787   | 0.9023  | 0.5379   | 0.001  |
| 0.1213        | 21.0  | 5754  | 0.1077          | 0.8323   | 0.7708   | 0.8907  | 0.5358   | 0.001  |
| 0.118         | 22.0  | 6028  | 0.1023          | 0.8446   | 0.7858   | 0.9041  | 0.5459   | 0.0001 |
| 0.118         | 23.0  | 6302  | 0.1009          | 0.8458   | 0.7888   | 0.8992  | 0.5546   | 0.0001 |
| 0.1102        | 24.0  | 6576  | 0.1000          | 0.8484   | 0.7925   | 0.9039  | 0.5546   | 0.0001 |
| 0.1102        | 25.0  | 6850  | 0.0974          | 0.8487   | 0.7919   | 0.9015  | 0.5557   | 0.0001 |
| 0.107         | 26.0  | 7124  | 0.1046          | 0.8510   | 0.7945   | 0.9057  | 0.5560   | 0.0001 |
| 0.107         | 27.0  | 7398  | 0.0967          | 0.8504   | 0.7968   | 0.9040  | 0.5602   | 0.0001 |
| 0.1051        | 28.0  | 7672  | 0.0941          | 0.8513   | 0.8002   | 0.9062  | 0.5550   | 0.0001 |
| 0.1051        | 29.0  | 7946  | 0.0941          | 0.8534   | 0.7979   | 0.9053  | 0.5612   | 0.0001 |
| 0.1039        | 30.0  | 8220  | 0.0949          | 0.8530   | 0.8028   | 0.9101  | 0.5553   | 0.0001 |
| 0.1039        | 31.0  | 8494  | 0.0942          | 0.8529   | 0.8005   | 0.9059  | 0.5637   | 0.0001 |
| 0.1025        | 32.0  | 8768  | 0.0951          | 0.8539   | 0.8021   | 0.9083  | 0.5623   | 0.0001 |
| 0.1016        | 33.0  | 9042  | 0.0974          | 0.8520   | 0.8013   | 0.9024  | 0.5612   | 0.0001 |
| 0.1016        | 34.0  | 9316  | 0.0926          | 0.8528   | 0.7981   | 0.9029  | 0.5609   | 0.0001 |
| 0.1004        | 35.0  | 9590  | 0.0924          | 0.8552   | 0.8059   | 0.9058  | 0.5689   | 0.0001 |
| 0.1004        | 36.0  | 9864  | 0.0927          | 0.8539   | 0.8087   | 0.9047  | 0.5630   | 0.0001 |
| 0.0993        | 37.0  | 10138 | 0.0915          | 0.8535   | 0.8050   | 0.9052  | 0.5665   | 0.0001 |
| 0.0993        | 38.0  | 10412 | 0.0923          | 0.8568   | 0.8059   | 0.9078  | 0.5672   | 0.0001 |
| 0.0992        | 39.0  | 10686 | 0.0930          | 0.8556   | 0.8078   | 0.9079  | 0.5654   | 0.0001 |
| 0.0992        | 40.0  | 10960 | 0.0932          | 0.8552   | 0.8109   | 0.9079  | 0.5612   | 0.0001 |
| 0.0984        | 41.0  | 11234 | 0.0922          | 0.8575   | 0.8114   | 0.9111  | 0.5598   | 0.0001 |
| 0.0973        | 42.0  | 11508 | 0.0927          | 0.8553   | 0.8147   | 0.9058  | 0.5672   | 0.0001 |
| 0.0973        | 43.0  | 11782 | 0.0911          | 0.8581   | 0.8148   | 0.9130  | 0.5637   | 0.0001 |
| 0.0973        | 44.0  | 12056 | 0.0915          | 0.8570   | 0.8127   | 0.9080  | 0.5665   | 0.0001 |
| 0.0973        | 45.0  | 12330 | 0.0908          | 0.8564   | 0.8094   | 0.9062  | 0.5640   | 0.0001 |
| 0.0954        | 46.0  | 12604 | 0.0900          | 0.8600   | 0.8157   | 0.9106  | 0.5696   | 0.0001 |
| 0.0954        | 47.0  | 12878 | 0.0901          | 0.8596   | 0.8204   | 0.9096  | 0.5672   | 0.0001 |
| 0.096         | 48.0  | 13152 | 0.0902          | 0.8594   | 0.8172   | 0.9082  | 0.5734   | 0.0001 |
| 0.096         | 49.0  | 13426 | 0.0903          | 0.8575   | 0.8128   | 0.9107  | 0.5717   | 0.0001 |
| 0.0939        | 50.0  | 13700 | 0.0897          | 0.8598   | 0.8164   | 0.9117  | 0.5696   | 0.0001 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.15.0
