---
library_name: transformers
license: apache-2.0
base_model: facebook/vit-msn-small
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-msn-small-wbc-blur-detector
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9565217391304348
---

<!-- 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-wbc-blur-detector

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: 0.1189
- Accuracy: 0.9565

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 60

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 3    | 0.6142          | 0.6304   |
| No log        | 2.0   | 6    | 0.3853          | 0.8696   |
| No log        | 3.0   | 9    | 0.4070          | 0.8261   |
| 0.494         | 4.0   | 12   | 0.1461          | 0.9348   |
| 0.494         | 5.0   | 15   | 0.1189          | 0.9565   |
| 0.494         | 6.0   | 18   | 0.1527          | 0.9457   |
| 0.2024        | 7.0   | 21   | 0.3323          | 0.9022   |
| 0.2024        | 8.0   | 24   | 0.1520          | 0.9457   |
| 0.2024        | 9.0   | 27   | 0.1572          | 0.9457   |
| 0.1419        | 10.0  | 30   | 0.1814          | 0.9348   |
| 0.1419        | 11.0  | 33   | 0.1778          | 0.9348   |
| 0.1419        | 12.0  | 36   | 0.1505          | 0.9348   |
| 0.1419        | 13.0  | 39   | 0.1891          | 0.9457   |
| 0.1053        | 14.0  | 42   | 0.7274          | 0.7935   |
| 0.1053        | 15.0  | 45   | 0.2669          | 0.9348   |
| 0.1053        | 16.0  | 48   | 0.2240          | 0.9348   |
| 0.3044        | 17.0  | 51   | 0.3497          | 0.8913   |
| 0.3044        | 18.0  | 54   | 0.2208          | 0.9348   |
| 0.3044        | 19.0  | 57   | 0.1733          | 0.9565   |
| 0.151         | 20.0  | 60   | 0.2038          | 0.9239   |
| 0.151         | 21.0  | 63   | 0.1282          | 0.9565   |
| 0.151         | 22.0  | 66   | 0.3231          | 0.9239   |
| 0.151         | 23.0  | 69   | 0.1565          | 0.9565   |
| 0.0875        | 24.0  | 72   | 0.1981          | 0.9457   |
| 0.0875        | 25.0  | 75   | 0.1974          | 0.9457   |
| 0.0875        | 26.0  | 78   | 0.2045          | 0.9457   |
| 0.0851        | 27.0  | 81   | 0.1841          | 0.9457   |
| 0.0851        | 28.0  | 84   | 0.2061          | 0.9565   |
| 0.0851        | 29.0  | 87   | 0.2077          | 0.9457   |
| 0.046         | 30.0  | 90   | 0.2199          | 0.9565   |
| 0.046         | 31.0  | 93   | 0.2038          | 0.9565   |
| 0.046         | 32.0  | 96   | 0.2077          | 0.9457   |
| 0.046         | 33.0  | 99   | 0.1877          | 0.9565   |
| 0.0533        | 34.0  | 102  | 0.2383          | 0.9348   |
| 0.0533        | 35.0  | 105  | 0.2571          | 0.9239   |
| 0.0533        | 36.0  | 108  | 0.2330          | 0.9565   |
| 0.0451        | 37.0  | 111  | 0.2420          | 0.9457   |
| 0.0451        | 38.0  | 114  | 0.2882          | 0.9239   |
| 0.0451        | 39.0  | 117  | 0.2386          | 0.9457   |
| 0.0401        | 40.0  | 120  | 0.2513          | 0.9348   |
| 0.0401        | 41.0  | 123  | 0.2672          | 0.9348   |
| 0.0401        | 42.0  | 126  | 0.2950          | 0.9457   |
| 0.0401        | 43.0  | 129  | 0.3232          | 0.9457   |
| 0.0329        | 44.0  | 132  | 0.3712          | 0.9239   |
| 0.0329        | 45.0  | 135  | 0.3529          | 0.9348   |
| 0.0329        | 46.0  | 138  | 0.2905          | 0.9457   |
| 0.0519        | 47.0  | 141  | 0.2670          | 0.9457   |
| 0.0519        | 48.0  | 144  | 0.2629          | 0.9457   |
| 0.0519        | 49.0  | 147  | 0.2761          | 0.9457   |
| 0.0281        | 50.0  | 150  | 0.3040          | 0.9457   |
| 0.0281        | 51.0  | 153  | 0.3191          | 0.9457   |
| 0.0281        | 52.0  | 156  | 0.3214          | 0.9457   |
| 0.0281        | 53.0  | 159  | 0.3132          | 0.9457   |
| 0.028         | 54.0  | 162  | 0.3115          | 0.9457   |
| 0.028         | 55.0  | 165  | 0.3116          | 0.9565   |
| 0.028         | 56.0  | 168  | 0.3225          | 0.9457   |
| 0.0361        | 57.0  | 171  | 0.3235          | 0.9457   |
| 0.0361        | 58.0  | 174  | 0.3200          | 0.9457   |
| 0.0361        | 59.0  | 177  | 0.3183          | 0.9457   |
| 0.0312        | 60.0  | 180  | 0.3181          | 0.9457   |


### Framework versions

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