transformers pytorch 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: dino-large-2023_12_19-kornia_img-size518_batch-size16_epochs20
  results: []
---

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# dino-large-2023_12_19-kornia_img-size518_batch-size16_epochs20

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: 0.1199
- F1 Micro: 0.8367
- F1 Macro: 0.8026
- Roc Auc: 0.9072
- Accuracy: 0.5354
- Learning Rate: 0.001

## 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: 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: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1 Micro | F1 Macro | Roc Auc | Accuracy | Rate  |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-------:|:--------:|:-----:|
| 0.2545        | 1.0   | 536   | 0.1936          | 0.7016   | 0.5553   | 0.7908  | 0.4134   | 0.01  |
| 0.2163        | 2.0   | 1072  | 0.1644          | 0.7672   | 0.6940   | 0.8669  | 0.4241   | 0.01  |
| 0.2142        | 3.0   | 1608  | 0.1720          | 0.7264   | 0.6226   | 0.8210  | 0.4259   | 0.01  |
| 0.2107        | 4.0   | 2144  | 0.1779          | 0.7311   | 0.6056   | 0.8442  | 0.4019   | 0.01  |
| 0.2117        | 5.0   | 2680  | 0.1835          | 0.7542   | 0.6745   | 0.8724  | 0.3834   | 0.01  |
| 0.2171        | 6.0   | 3216  | 0.1732          | 0.7347   | 0.5959   | 0.8236  | 0.4209   | 0.01  |
| 0.2178        | 7.0   | 3752  | 0.2698          | 0.7253   | 0.5932   | 0.8165  | 0.3905   | 0.01  |
| 0.2177        | 8.0   | 4288  | 0.1940          | 0.7360   | 0.6280   | 0.8286  | 0.4119   | 0.01  |
| 0.212         | 9.0   | 4824  | 0.1455          | 0.7993   | 0.7491   | 0.8757  | 0.4898   | 0.001 |
| 0.1761        | 10.0  | 5360  | 0.1357          | 0.8116   | 0.7661   | 0.8733  | 0.5123   | 0.001 |
| 0.1681        | 11.0  | 5896  | 0.1386          | 0.8152   | 0.7753   | 0.8791  | 0.5166   | 0.001 |
| 0.1579        | 12.0  | 6432  | 0.1820          | 0.8220   | 0.7827   | 0.8919  | 0.5163   | 0.001 |
| 0.1553        | 13.0  | 6968  | 0.1228          | 0.8297   | 0.7908   | 0.8898  | 0.5327   | 0.001 |
| 0.1512        | 14.0  | 7504  | 0.1233          | 0.8258   | 0.7815   | 0.8845  | 0.5302   | 0.001 |
| 0.1508        | 15.0  | 8040  | 0.1248          | 0.8179   | 0.7682   | 0.8740  | 0.5305   | 0.001 |
| 0.1499        | 16.0  | 8576  | 0.1193          | 0.8277   | 0.7903   | 0.8806  | 0.5395   | 0.001 |
| 0.1435        | 17.0  | 9112  | 0.1159          | 0.8381   | 0.7996   | 0.9037  | 0.5380   | 0.001 |
| 0.1463        | 18.0  | 9648  | 0.1166          | 0.8393   | 0.8033   | 0.8957  | 0.5481   | 0.001 |
| 0.1423        | 19.0  | 10184 | 0.1216          | 0.8327   | 0.8009   | 0.8865  | 0.5459   | 0.001 |
| 0.1444        | 20.0  | 10720 | 0.1171          | 0.8383   | 0.8020   | 0.8908  | 0.5509   | 0.001 |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
