---
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-cells-separated-dataset-agregates-25
  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: 0.9400918591493044
---

<!-- 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-cells-separated-dataset-agregates-25

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: 0.1790
- Accuracy: 0.9401

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.351         | 0.9937  | 119  | 0.2523          | 0.9151   |
| 0.3364        | 1.9958  | 239  | 0.2355          | 0.9195   |
| 0.2999        | 2.9979  | 359  | 0.2384          | 0.9169   |
| 0.2861        | 4.0     | 479  | 0.1902          | 0.9341   |
| 0.3014        | 4.9937  | 598  | 0.2154          | 0.9290   |
| 0.292         | 5.9958  | 718  | 0.1764          | 0.9383   |
| 0.2441        | 6.9979  | 838  | 0.1894          | 0.9348   |
| 0.2416        | 8.0     | 958  | 0.1913          | 0.9349   |
| 0.2642        | 8.9937  | 1077 | 0.1738          | 0.9385   |
| 0.2482        | 9.9958  | 1197 | 0.1911          | 0.9371   |
| 0.2279        | 10.9979 | 1317 | 0.1867          | 0.9381   |
| 0.2331        | 12.0    | 1437 | 0.1814          | 0.9389   |
| 0.2208        | 12.9937 | 1556 | 0.1790          | 0.9401   |
| 0.2326        | 13.9958 | 1676 | 0.1926          | 0.9366   |
| 0.1899        | 14.9979 | 1796 | 0.1975          | 0.9372   |
| 0.1822        | 16.0    | 1916 | 0.2052          | 0.9352   |
| 0.1837        | 16.9937 | 2035 | 0.2078          | 0.9364   |
| 0.1712        | 17.9958 | 2155 | 0.2345          | 0.9288   |
| 0.1715        | 18.9979 | 2275 | 0.2156          | 0.9368   |
| 0.1516        | 20.0    | 2395 | 0.2279          | 0.9368   |
| 0.1504        | 20.9937 | 2514 | 0.2213          | 0.9382   |
| 0.139         | 21.9958 | 2634 | 0.2247          | 0.9370   |
| 0.1264        | 22.9979 | 2754 | 0.2357          | 0.9384   |
| 0.1266        | 24.0    | 2874 | 0.2360          | 0.9381   |
| 0.1144        | 24.8434 | 2975 | 0.2370          | 0.9375   |


### Framework versions

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