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# vit-msn-small-lateral_flow_ivalidation_train_test_7

This model is a fine-tuned version of [Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_7](https://huggingface.co/Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_7) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4160
- Accuracy: 0.8791

## 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: 100
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9231  | 3    | 0.4160          | 0.8791   |
| No log        | 1.8462  | 6    | 0.4668          | 0.8388   |
| No log        | 2.7692  | 9    | 0.5433          | 0.8022   |
| 0.3869        | 4.0     | 13   | 0.5052          | 0.8168   |
| 0.3869        | 4.9231  | 16   | 0.4591          | 0.8571   |
| 0.3869        | 5.8462  | 19   | 0.4820          | 0.8278   |
| 0.3658        | 6.7692  | 22   | 0.4953          | 0.8095   |
| 0.3658        | 8.0     | 26   | 0.4497          | 0.8608   |
| 0.3658        | 8.9231  | 29   | 0.4686          | 0.8315   |
| 0.3439        | 9.8462  | 32   | 0.4506          | 0.8608   |
| 0.3439        | 10.7692 | 35   | 0.4859          | 0.8168   |
| 0.3439        | 12.0    | 39   | 0.4929          | 0.8168   |
| 0.3416        | 12.9231 | 42   | 0.4957          | 0.8059   |
| 0.3416        | 13.8462 | 45   | 0.5229          | 0.7875   |
| 0.3416        | 14.7692 | 48   | 0.4473          | 0.8535   |
| 0.324         | 16.0    | 52   | 0.5260          | 0.8059   |
| 0.324         | 16.9231 | 55   | 0.4582          | 0.8462   |
| 0.324         | 17.8462 | 58   | 0.5299          | 0.7839   |
| 0.3273        | 18.7692 | 61   | 0.4947          | 0.8205   |
| 0.3273        | 20.0    | 65   | 0.5393          | 0.7692   |
| 0.3273        | 20.9231 | 68   | 0.4916          | 0.8278   |
| 0.3397        | 21.8462 | 71   | 0.5360          | 0.7802   |
| 0.3397        | 22.7692 | 74   | 0.5661          | 0.7656   |
| 0.3397        | 24.0    | 78   | 0.6354          | 0.7216   |
| 0.3344        | 24.9231 | 81   | 0.6782          | 0.7033   |
| 0.3344        | 25.8462 | 84   | 0.5704          | 0.7582   |
| 0.3344        | 26.7692 | 87   | 0.6537          | 0.6777   |
| 0.3325        | 28.0    | 91   | 0.4798          | 0.8425   |
| 0.3325        | 28.9231 | 94   | 0.5158          | 0.8059   |
| 0.3325        | 29.8462 | 97   | 0.5408          | 0.7912   |
| 0.3283        | 30.7692 | 100  | 0.5964          | 0.7399   |
| 0.3283        | 32.0    | 104  | 0.5069          | 0.8205   |
| 0.3283        | 32.9231 | 107  | 0.5396          | 0.7875   |
| 0.3229        | 33.8462 | 110  | 0.5203          | 0.7985   |
| 0.3229        | 34.7692 | 113  | 0.5464          | 0.7875   |
| 0.3229        | 36.0    | 117  | 0.5890          | 0.7509   |
| 0.3207        | 36.9231 | 120  | 0.5080          | 0.8132   |
| 0.3207        | 37.8462 | 123  | 0.4944          | 0.8168   |
| 0.3207        | 38.7692 | 126  | 0.4968          | 0.8095   |
| 0.3286        | 40.0    | 130  | 0.4874          | 0.8132   |
| 0.3286        | 40.9231 | 133  | 0.5013          | 0.8059   |
| 0.3286        | 41.8462 | 136  | 0.5329          | 0.7656   |
| 0.3286        | 42.7692 | 139  | 0.6199          | 0.6996   |
| 0.3154        | 44.0    | 143  | 0.4854          | 0.8059   |
| 0.3154        | 44.9231 | 146  | 0.5545          | 0.7509   |
| 0.3154        | 45.8462 | 149  | 0.5267          | 0.7729   |
| 0.3119        | 46.7692 | 152  | 0.5214          | 0.7802   |
| 0.3119        | 48.0    | 156  | 0.5265          | 0.7839   |
| 0.3119        | 48.9231 | 159  | 0.5137          | 0.7985   |
| 0.3036        | 49.8462 | 162  | 0.5354          | 0.7839   |
| 0.3036        | 50.7692 | 165  | 0.5269          | 0.7875   |
| 0.3036        | 52.0    | 169  | 0.5797          | 0.7399   |
| 0.2995        | 52.9231 | 172  | 0.6258          | 0.7179   |
| 0.2995        | 53.8462 | 175  | 0.5512          | 0.7692   |
| 0.2995        | 54.7692 | 178  | 0.5517          | 0.7619   |
| 0.306         | 56.0    | 182  | 0.5590          | 0.7546   |
| 0.306         | 56.9231 | 185  | 0.5514          | 0.7619   |
| 0.306         | 57.8462 | 188  | 0.5597          | 0.7509   |
| 0.2989        | 58.7692 | 191  | 0.5957          | 0.7326   |
| 0.2989        | 60.0    | 195  | 0.5366          | 0.7766   |
| 0.2989        | 60.9231 | 198  | 0.5465          | 0.7729   |
| 0.2931        | 61.8462 | 201  | 0.6171          | 0.7253   |
| 0.2931        | 62.7692 | 204  | 0.5768          | 0.7509   |
| 0.2931        | 64.0    | 208  | 0.5706          | 0.7509   |
| 0.299         | 64.9231 | 211  | 0.5962          | 0.7363   |
| 0.299         | 65.8462 | 214  | 0.6220          | 0.7216   |
| 0.299         | 66.7692 | 217  | 0.5929          | 0.7363   |
| 0.2969        | 68.0    | 221  | 0.6136          | 0.7253   |
| 0.2969        | 68.9231 | 224  | 0.6092          | 0.7289   |
| 0.2969        | 69.8462 | 227  | 0.6029          | 0.7253   |
| 0.3015        | 70.7692 | 230  | 0.5356          | 0.7766   |
| 0.3015        | 72.0    | 234  | 0.5376          | 0.7692   |
| 0.3015        | 72.9231 | 237  | 0.5886          | 0.7436   |
| 0.2919        | 73.8462 | 240  | 0.5869          | 0.7436   |
| 0.2919        | 74.7692 | 243  | 0.5846          | 0.7473   |
| 0.2919        | 76.0    | 247  | 0.5507          | 0.7656   |
| 0.288         | 76.9231 | 250  | 0.5801          | 0.7509   |
| 0.288         | 77.8462 | 253  | 0.6077          | 0.7399   |
| 0.288         | 78.7692 | 256  | 0.5848          | 0.7436   |
| 0.2951        | 80.0    | 260  | 0.5435          | 0.7692   |
| 0.2951        | 80.9231 | 263  | 0.5638          | 0.7656   |
| 0.2951        | 81.8462 | 266  | 0.5795          | 0.7399   |
| 0.2951        | 82.7692 | 269  | 0.5774          | 0.7509   |
| 0.2875        | 84.0    | 273  | 0.5703          | 0.7509   |
| 0.2875        | 84.9231 | 276  | 0.5713          | 0.7509   |
| 0.2875        | 85.8462 | 279  | 0.5784          | 0.7473   |
| 0.2855        | 86.7692 | 282  | 0.5904          | 0.7436   |
| 0.2855        | 88.0    | 286  | 0.5917          | 0.7326   |
| 0.2855        | 88.9231 | 289  | 0.5860          | 0.7473   |
| 0.2964        | 89.8462 | 292  | 0.5858          | 0.7473   |
| 0.2964        | 90.7692 | 295  | 0.5823          | 0.7436   |
| 0.2964        | 92.0    | 299  | 0.5817          | 0.7436   |
| 0.291         | 92.3077 | 300  | 0.5816          | 0.7436   |


### Framework versions

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

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