---
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-corect_cleaned_dataset_lateral_flow_ivalidation
  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: 0.9230769230769231
---

<!-- 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-corect_cleaned_dataset_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: 0.2318
- Accuracy: 0.9231

## 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: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9231  | 3    | 0.6468          | 0.5604   |
| No log        | 1.8462  | 6    | 0.4227          | 0.8462   |
| No log        | 2.7692  | 9    | 0.3390          | 0.8608   |
| 0.5336        | 4.0     | 13   | 0.3115          | 0.8864   |
| 0.5336        | 4.9231  | 16   | 0.2986          | 0.8938   |
| 0.5336        | 5.8462  | 19   | 0.2318          | 0.9231   |
| 0.3565        | 6.7692  | 22   | 0.2767          | 0.9121   |
| 0.3565        | 8.0     | 26   | 0.2490          | 0.9084   |
| 0.3565        | 8.9231  | 29   | 0.3151          | 0.8938   |
| 0.3166        | 9.8462  | 32   | 0.2404          | 0.9231   |
| 0.3166        | 10.7692 | 35   | 0.2520          | 0.9158   |
| 0.3166        | 12.0    | 39   | 0.2515          | 0.9048   |
| 0.2657        | 12.9231 | 42   | 0.2344          | 0.9121   |
| 0.2657        | 13.8462 | 45   | 0.2187          | 0.9194   |
| 0.2657        | 14.7692 | 48   | 0.2289          | 0.9194   |
| 0.259         | 16.0    | 52   | 0.2251          | 0.9194   |
| 0.259         | 16.9231 | 55   | 0.2238          | 0.9231   |
| 0.259         | 17.8462 | 58   | 0.2312          | 0.9121   |
| 0.2514        | 18.4615 | 60   | 0.2305          | 0.9084   |


### Framework versions

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