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_15-with_data_aug_batch-size32_epochs20_freeze
  results: []
---

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# dinov2-large-2024_01_15-with_data_aug_batch-size32_epochs20_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: 0.0891
- F1 Micro: 0.8422
- F1 Macro: 0.7067
- Roc Auc: 0.8958
- Accuracy: 0.5445
- 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: 0.01
- 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: 20

### Training results

| Training Loss | Epoch | Step | Accuracy | F1 Macro | F1 Micro | Validation Loss | Roc Auc | Rate   |
|:-------------:|:-----:|:----:|:--------:|:--------:|:--------:|:---------------:|:-------:|:------:|
| No log        | 1.0   | 274  | 0.4728   | 0.5944   | 0.7681   | 0.1364          | 0.8536  | 0.001  |
| 0.2417        | 2.0   | 548  | 0.5028   | 0.6565   | 0.8040   | 0.1118          | 0.8701  | 0.001  |
| 0.2417        | 3.0   | 822  | 0.5122   | 0.6697   | 0.8124   | 0.1061          | 0.8763  | 0.001  |
| 0.1314        | 4.0   | 1096 | 0.5003   | 0.6643   | 0.8168   | 0.1062          | 0.8832  | 0.001  |
| 0.1314        | 5.0   | 1370 | 0.5178   | 0.6736   | 0.8176   | 0.1032          | 0.8783  | 0.001  |
| 0.1235        | 6.0   | 1644 | 0.5335   | 0.6928   | 0.8255   | 0.1027          | 0.8906  | 0.001  |
| 0.1235        | 7.0   | 1918 | 0.5237   | 0.6767   | 0.8205   | 0.1027          | 0.8774  | 0.001  |
| 0.1196        | 8.0   | 2192 | 0.5181   | 0.6758   | 0.8176   | 0.1027          | 0.8775  | 0.001  |
| 0.1196        | 9.0   | 2466 | 0.5335   | 0.6807   | 0.8224   | 0.0994          | 0.8765  | 0.001  |
| 0.117         | 10.0  | 2740 | 0.5167   | 0.6870   | 0.8283   | 0.1007          | 0.8937  | 0.001  |
| 0.1163        | 11.0  | 3014 | 0.5195   | 0.6925   | 0.8298   | 0.0971          | 0.8898  | 0.001  |
| 0.1163        | 12.0  | 3288 | 0.5230   | 0.7006   | 0.8282   | 0.0987          | 0.8861  | 0.001  |
| 0.1156        | 13.0  | 3562 | 0.5342   | 0.7065   | 0.8275   | 0.1017          | 0.8903  | 0.001  |
| 0.1156        | 14.0  | 3836 | 0.5276   | 0.6968   | 0.8243   | 0.1224          | 0.8851  | 0.001  |
| 0.1137        | 15.0  | 4110 | 0.5300   | 0.6958   | 0.8295   | 0.0981          | 0.8904  | 0.001  |
| 0.1137        | 16.0  | 4384 | 0.5398   | 0.7179   | 0.8412   | 0.0919          | 0.8981  | 0.0001 |
| 0.1091        | 17.0  | 4658 | 0.5433   | 0.7235   | 0.8431   | 0.0924          | 0.8997  | 0.0001 |
| 0.1091        | 18.0  | 4932 | 0.5447   | 0.7166   | 0.8448   | 0.0904          | 0.8984  | 0.0001 |
| 0.1026        | 19.0  | 5206 | 0.0903   | 0.8448   | 0.7248   | 0.8963          | 0.5443  | 0.0001 |
| 0.1026        | 20.0  | 5480 | 0.0887   | 0.8439   | 0.7176   | 0.8971          | 0.5422  | 0.0001 |


### Framework versions

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