
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-msn-small-beta-fia-individually-enhanced_test_1

This model is a fine-tuned version of [facebook/vit-msn-small](https://huggingface.co/facebook/vit-msn-small) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7134
- Accuracy: 0.8028

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 50
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.5714  | 1    | 1.8493          | 0.1268   |
| No log        | 1.7143  | 3    | 1.7331          | 0.1268   |
| No log        | 2.8571  | 5    | 1.4744          | 0.1268   |
| No log        | 4.0     | 7    | 1.1333          | 0.2958   |
| No log        | 4.5714  | 8    | 0.9675          | 0.6690   |
| 1.2028        | 5.7143  | 10   | 0.7486          | 0.8028   |
| 1.2028        | 6.8571  | 12   | 0.7134          | 0.8028   |
| 1.2028        | 8.0     | 14   | 0.7447          | 0.7958   |
| 1.2028        | 8.5714  | 15   | 0.7686          | 0.8028   |
| 1.2028        | 9.7143  | 17   | 0.7991          | 0.8028   |
| 1.2028        | 10.8571 | 19   | 0.7821          | 0.8028   |
| 0.7435        | 12.0    | 21   | 0.7589          | 0.8028   |
| 0.7435        | 12.5714 | 22   | 0.7497          | 0.8028   |
| 0.7435        | 13.7143 | 24   | 0.7412          | 0.8028   |
| 0.7435        | 14.8571 | 26   | 0.7626          | 0.8028   |
| 0.7435        | 16.0    | 28   | 0.7868          | 0.8028   |
| 0.7435        | 16.5714 | 29   | 0.7903          | 0.7958   |
| 0.6624        | 17.7143 | 31   | 0.7725          | 0.7958   |
| 0.6624        | 18.8571 | 33   | 0.7456          | 0.7887   |
| 0.6624        | 20.0    | 35   | 0.7403          | 0.8099   |
| 0.6624        | 20.5714 | 36   | 0.7444          | 0.8239   |
| 0.6624        | 21.7143 | 38   | 0.7515          | 0.8099   |
| 0.6099        | 22.8571 | 40   | 0.7579          | 0.8099   |
| 0.6099        | 24.0    | 42   | 0.7629          | 0.8099   |
| 0.6099        | 24.5714 | 43   | 0.7650          | 0.8169   |
| 0.6099        | 25.7143 | 45   | 0.7621          | 0.8169   |
| 0.6099        | 26.8571 | 47   | 0.7571          | 0.8099   |
| 0.6099        | 28.0    | 49   | 0.7551          | 0.8099   |
| 0.5882        | 28.5714 | 50   | 0.7551          | 0.8099   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1

extra incomplete normal