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# dinov2-small-fer

This model is a fine-tuned version of [facebook/dinov2-small-imagenet1k-1-layer](https://huggingface.co/facebook/dinov2-small-imagenet1k-1-layer) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8495
- Accuracy: 0.6964
- F1 angry: 0.6087
- F1 disgust: 0.5556
- F1 fear: 0.5091
- F1 happy: 0.8972
- F1 neutral: 0.6705
- F1 sad: 0.5893
- F1 surprise: 0.7923

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 angry | F1 disgust | F1 fear | F1 happy | F1 neutral | F1 sad | F1 surprise |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:----------:|:-------:|:--------:|:----------:|:------:|:-----------:|
| No log        | 1.0   | 449  | 1.1298          | 0.5659   | 0.3647   | 0.0        | 0.3323  | 0.8466   | 0.5333     | 0.4221 | 0.6871      |
| 1.3025        | 2.0   | 898  | 0.9742          | 0.6322   | 0.5274   | 0.2029     | 0.4050  | 0.8734   | 0.6080     | 0.5073 | 0.7528      |
| 1.0413        | 3.0   | 1347 | 0.9270          | 0.6499   | 0.5517   | 0.4157     | 0.4662  | 0.8846   | 0.6287     | 0.4934 | 0.7694      |
| 0.954         | 4.0   | 1796 | 0.9256          | 0.6537   | 0.5383   | 0.3975     | 0.4553  | 0.8801   | 0.6342     | 0.5153 | 0.7714      |
| 0.8994        | 5.0   | 2245 | 0.8979          | 0.6655   | 0.5820   | 0.3896     | 0.4075  | 0.8881   | 0.6377     | 0.5610 | 0.7759      |
| 0.8501        | 6.0   | 2694 | 0.8805          | 0.6776   | 0.6003   | 0.4599     | 0.4657  | 0.8932   | 0.6599     | 0.5382 | 0.7816      |
| 0.7972        | 7.0   | 3143 | 0.9028          | 0.6723   | 0.5918   | 0.4094     | 0.3981  | 0.8890   | 0.6547     | 0.5487 | 0.7811      |
| 0.7676        | 8.0   | 3592 | 0.8530          | 0.6884   | 0.6152   | 0.5085     | 0.5093  | 0.8922   | 0.6667     | 0.5447 | 0.7933      |
| 0.7138        | 9.0   | 4041 | 0.8927          | 0.6761   | 0.5794   | 0.5771     | 0.4487  | 0.8852   | 0.6494     | 0.5670 | 0.7833      |
| 0.7138        | 10.0  | 4490 | 0.8556          | 0.6896   | 0.6114   | 0.5668     | 0.4927  | 0.8957   | 0.6613     | 0.5799 | 0.7859      |
| 0.6799        | 11.0  | 4939 | 0.8477          | 0.6973   | 0.6174   | 0.5672     | 0.5118  | 0.8974   | 0.6694     | 0.5843 | 0.7900      |
| 0.643         | 12.0  | 5388 | 0.8495          | 0.6964   | 0.6087   | 0.5556     | 0.5091  | 0.8972   | 0.6705     | 0.5893 | 0.7923      |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0

angry disgust fear happy neutral sad surprise