transformers pytorch dinov2 multilabel-image-classification multilabel generated_from_trainer eng base_model:facebook/dinov2-base license:apache-2.0 endpoints_compatible region:us
---
language:
- eng
license: apache-2.0
base_model: facebook/dinov2-base
tags:
- multilabel-image-classification
- multilabel
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dino-base-2023_12_18-kornia_img-size518_batch-size32_epochs20
  results: []
---

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# dino-base-2023_12_18-kornia_img-size518_batch-size32_epochs20

This model is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) on the multilabel_complete_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1422
- F1 Micro: 0.7786
- F1 Macro: 0.7231
- Roc Auc: 0.8542
- Accuracy: 0.4586
- Learning Rate: 0.01

## 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 | Validation Loss | F1 Micro | F1 Macro | Roc Auc | Accuracy | Rate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-------:|:--------:|:----:|
| No log        | 1.0   | 268  | 0.1853          | 0.6692   | 0.5725   | 0.7700  | 0.4084   | 0.01 |
| 0.2132        | 2.0   | 536  | 0.1678          | 0.7630   | 0.7066   | 0.8729  | 0.4134   | 0.01 |
| 0.2132        | 3.0   | 804  | 0.1568          | 0.7665   | 0.7008   | 0.8456  | 0.4541   | 0.01 |
| 0.1913        | 4.0   | 1072 | 0.1496          | 0.7900   | 0.7401   | 0.8786  | 0.4509   | 0.01 |
| 0.1913        | 5.0   | 1340 | 0.1668          | 0.7692   | 0.7355   | 0.8765  | 0.4091   | 0.01 |
| 0.1899        | 6.0   | 1608 | 0.1519          | 0.7619   | 0.6472   | 0.8352  | 0.4605   | 0.01 |
| 0.1899        | 7.0   | 1876 | 0.1590          | 0.7725   | 0.6881   | 0.8654  | 0.4391   | 0.01 |
| 0.188         | 8.0   | 2144 | 0.1490          | 0.7812   | 0.6946   | 0.8642  | 0.4459   | 0.01 |
| 0.188         | 9.0   | 2412 | 0.1493          | 0.7887   | 0.7115   | 0.8765  | 0.4670   | 0.01 |
| 0.1888        | 10.0  | 2680 | 0.1444          | 0.7744   | 0.7014   | 0.8445  | 0.4720   | 0.01 |
| 0.1888        | 11.0  | 2948 | 0.1582          | 0.7652   | 0.6895   | 0.8498  | 0.4348   | 0.01 |
| 0.1888        | 12.0  | 3216 | 0.1536          | 0.7491   | 0.6946   | 0.8176  | 0.4616   | 0.01 |
| 0.1888        | 13.0  | 3484 | 0.1514          | 0.7728   | 0.6920   | 0.8503  | 0.4555   | 0.01 |
| 0.1886        | 14.0  | 3752 | 0.1668          | 0.6863   | 0.5593   | 0.7725  | 0.4355   | 0.01 |
| 0.1906        | 15.0  | 4020 | 0.1524          | 0.7589   | 0.6660   | 0.8395  | 0.4534   | 0.01 |
| 0.1906        | 16.0  | 4288 | 0.1429          | 0.7849   | 0.7240   | 0.8546  | 0.4762   | 0.01 |
| 0.1879        | 17.0  | 4556 | 0.1711          | 0.7453   | 0.6093   | 0.8492  | 0.4123   | 0.01 |
| 0.1879        | 18.0  | 4824 | 0.1588          | 0.7304   | 0.5857   | 0.8062  | 0.4373   | 0.01 |
| 0.1888        | 19.0  | 5092 | 0.1634          | 0.7428   | 0.6950   | 0.8220  | 0.4466   | 0.01 |
| 0.1888        | 20.0  | 5360 | 0.1485          | 0.7788   | 0.7272   | 0.8566  | 0.4645   | 0.01 |


### Framework versions

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