---
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-classifier-0316-cleaned-dataset-10
  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: 0.893532776066872
---

<!-- 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-classifier-0316-cleaned-dataset-10

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: 0.3538
- Accuracy: 0.8935

## 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: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5193        | 1.0   | 16   | 0.6823          | 0.7945   |
| 0.5339        | 2.0   | 32   | 0.4553          | 0.8438   |
| 0.4778        | 3.0   | 48   | 0.4525          | 0.8478   |
| 0.4253        | 4.0   | 64   | 0.4077          | 0.8473   |
| 0.4086        | 5.0   | 80   | 0.4218          | 0.8575   |
| 0.3673        | 6.0   | 96   | 0.4002          | 0.8693   |
| 0.3275        | 7.0   | 112  | 0.3302          | 0.8773   |
| 0.3231        | 8.0   | 128  | 0.3672          | 0.8803   |
| 0.302         | 9.0   | 144  | 0.3363          | 0.8900   |
| 0.3122        | 10.0  | 160  | 0.3284          | 0.8843   |
| 0.2686        | 11.0  | 176  | 0.3317          | 0.8874   |
| 0.2786        | 12.0  | 192  | 0.3660          | 0.8883   |
| 0.2338        | 13.0  | 208  | 0.3520          | 0.8834   |
| 0.2466        | 14.0  | 224  | 0.3414          | 0.8896   |
| 0.2296        | 15.0  | 240  | 0.3531          | 0.8874   |
| 0.1961        | 16.0  | 256  | 0.3844          | 0.8847   |
| 0.2056        | 17.0  | 272  | 0.3705          | 0.8900   |
| 0.197         | 18.0  | 288  | 0.3538          | 0.8935   |
| 0.1748        | 19.0  | 304  | 0.3717          | 0.8887   |
| 0.1807        | 20.0  | 320  | 0.4075          | 0.8843   |
| 0.177         | 21.0  | 336  | 0.3881          | 0.8830   |
| 0.1433        | 22.0  | 352  | 0.4014          | 0.8856   |
| 0.1522        | 23.0  | 368  | 0.3918          | 0.8874   |
| 0.1322        | 24.0  | 384  | 0.4199          | 0.8905   |
| 0.1396        | 25.0  | 400  | 0.4142          | 0.8896   |


### Framework versions

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