---
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-cropped-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: 0.8854679802955665
---

<!-- 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-cropped-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: 0.3986
- Accuracy: 0.8855

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3709        | 1.0   | 17   | 0.6977          | 0.8050   |
| 0.5673        | 2.0   | 34   | 0.5949          | 0.8099   |
| 0.5227        | 3.0   | 51   | 0.6152          | 0.7931   |
| 0.4958        | 4.0   | 68   | 0.4351          | 0.8436   |
| 0.4402        | 5.0   | 85   | 0.3777          | 0.8580   |
| 0.3878        | 6.0   | 102  | 0.3970          | 0.8699   |
| 0.3646        | 7.0   | 119  | 0.3793          | 0.8641   |
| 0.3452        | 8.0   | 136  | 0.3550          | 0.8805   |
| 0.344         | 9.0   | 153  | 0.4003          | 0.8736   |
| 0.3365        | 10.0  | 170  | 0.3654          | 0.8830   |
| 0.3223        | 11.0  | 187  | 0.3571          | 0.8764   |
| 0.2819        | 12.0  | 204  | 0.3665          | 0.8789   |
| 0.2998        | 13.0  | 221  | 0.3609          | 0.8838   |
| 0.2959        | 14.0  | 238  | 0.4335          | 0.8719   |
| 0.2662        | 15.0  | 255  | 0.4245          | 0.8785   |
| 0.2668        | 16.0  | 272  | 0.3760          | 0.8846   |
| 0.2576        | 17.0  | 289  | 0.3728          | 0.8830   |
| 0.2398        | 18.0  | 306  | 0.4192          | 0.8814   |
| 0.2278        | 19.0  | 323  | 0.4156          | 0.8805   |
| 0.2033        | 20.0  | 340  | 0.4159          | 0.8851   |
| 0.2037        | 21.0  | 357  | 0.3986          | 0.8855   |
| 0.1934        | 22.0  | 374  | 0.4220          | 0.8822   |
| 0.1983        | 23.0  | 391  | 0.4159          | 0.8855   |
| 0.1746        | 24.0  | 408  | 0.4179          | 0.8855   |
| 0.1776        | 25.0  | 425  | 0.4247          | 0.8834   |


### Framework versions

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