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# vit-msn-small-ultralytics_yolo_cropped_lateral_flow_ivalidation

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: 0.4550
- Accuracy: 0.8373

## 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: 5e-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: 0.1
- num_epochs: 40

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9032  | 7    | 0.6079          | 0.7263   |
| 0.4464        | 1.9355  | 15   | 0.4912          | 0.8107   |
| 0.3464        | 2.9677  | 23   | 0.6082          | 0.6820   |
| 0.2864        | 4.0     | 31   | 0.5636          | 0.7234   |
| 0.2864        | 4.9032  | 38   | 0.4431          | 0.8121   |
| 0.2617        | 5.9355  | 46   | 0.5066          | 0.7322   |
| 0.2504        | 6.9677  | 54   | 0.4550          | 0.8373   |
| 0.2319        | 8.0     | 62   | 0.7023          | 0.6686   |
| 0.2319        | 8.9032  | 69   | 0.6887          | 0.6346   |
| 0.2338        | 9.9355  | 77   | 0.5075          | 0.8107   |
| 0.2163        | 10.9677 | 85   | 0.6170          | 0.7189   |
| 0.2024        | 12.0    | 93   | 0.7783          | 0.6139   |
| 0.2027        | 12.9032 | 100  | 0.9525          | 0.5059   |
| 0.2027        | 13.9355 | 108  | 0.7353          | 0.6805   |
| 0.2086        | 14.9677 | 116  | 0.7734          | 0.6479   |
| 0.1921        | 16.0    | 124  | 0.9112          | 0.5251   |
| 0.1827        | 16.9032 | 131  | 0.6997          | 0.6997   |
| 0.1827        | 17.9355 | 139  | 0.7572          | 0.6731   |
| 0.1854        | 18.9677 | 147  | 0.6843          | 0.7041   |
| 0.172         | 20.0    | 155  | 0.7237          | 0.6997   |
| 0.1703        | 20.9032 | 162  | 0.7698          | 0.6598   |
| 0.1587        | 21.9355 | 170  | 0.7597          | 0.6420   |
| 0.1587        | 22.9677 | 178  | 0.8517          | 0.5976   |
| 0.1673        | 24.0    | 186  | 0.6763          | 0.6672   |
| 0.1474        | 24.9032 | 193  | 0.8353          | 0.6420   |
| 0.1512        | 25.9355 | 201  | 0.7117          | 0.6953   |
| 0.1512        | 26.9677 | 209  | 0.8383          | 0.6169   |
| 0.1427        | 28.0    | 217  | 1.0619          | 0.5399   |
| 0.1501        | 28.9032 | 224  | 0.7946          | 0.6760   |
| 0.1325        | 29.9355 | 232  | 1.0962          | 0.5222   |
| 0.1314        | 30.9677 | 240  | 0.8824          | 0.6183   |
| 0.1314        | 32.0    | 248  | 0.8409          | 0.6331   |
| 0.1294        | 32.9032 | 255  | 0.8754          | 0.6021   |
| 0.1204        | 33.9355 | 263  | 0.8036          | 0.6716   |
| 0.1218        | 34.9677 | 271  | 0.8477          | 0.6568   |
| 0.1218        | 36.0    | 279  | 0.8739          | 0.6331   |
| 0.1217        | 36.1290 | 280  | 0.8748          | 0.6331   |


### Framework versions

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

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