---
library_name: transformers
license: apache-2.0
base_model: Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_6
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-msn-small-lateral_flow_ivalidation_train_test_7
  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: 0.8754578754578755
---

<!-- 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-lateral_flow_ivalidation_train_test_7

This model is a fine-tuned version of [Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_6](https://huggingface.co/Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_6) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4368
- Accuracy: 0.8755

## 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-07
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: reduce_lr_on_plateau
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 100

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.6154  | 1    | 0.4368          | 0.8755   |
| No log        | 1.8462  | 3    | 0.4440          | 0.8681   |
| No log        | 2.4615  | 4    | 0.4470          | 0.8645   |
| No log        | 3.6923  | 6    | 0.4443          | 0.8645   |
| No log        | 4.9231  | 8    | 0.4393          | 0.8645   |
| No log        | 5.5385  | 9    | 0.4372          | 0.8681   |
| 0.3118        | 6.7692  | 11   | 0.4340          | 0.8645   |
| 0.3118        | 8.0     | 13   | 0.4319          | 0.8608   |
| 0.3118        | 8.6154  | 14   | 0.4313          | 0.8608   |
| 0.3118        | 9.8462  | 16   | 0.4312          | 0.8681   |
| 0.3118        | 10.4615 | 17   | 0.4314          | 0.8718   |
| 0.3118        | 11.6923 | 19   | 0.4306          | 0.8718   |
| 0.3019        | 12.9231 | 21   | 0.4294          | 0.8718   |
| 0.3019        | 13.5385 | 22   | 0.4290          | 0.8718   |
| 0.3019        | 14.7692 | 24   | 0.4262          | 0.8718   |
| 0.3019        | 16.0    | 26   | 0.4223          | 0.8718   |
| 0.3019        | 16.6154 | 27   | 0.4204          | 0.8718   |
| 0.3019        | 17.8462 | 29   | 0.4170          | 0.8718   |
| 0.2922        | 18.4615 | 30   | 0.4160          | 0.8718   |
| 0.2922        | 19.6923 | 32   | 0.4161          | 0.8718   |
| 0.2922        | 20.9231 | 34   | 0.4161          | 0.8718   |
| 0.2922        | 21.5385 | 35   | 0.4162          | 0.8718   |
| 0.2922        | 22.7692 | 37   | 0.4164          | 0.8718   |
| 0.2922        | 24.0    | 39   | 0.4166          | 0.8718   |
| 0.2993        | 24.6154 | 40   | 0.4168          | 0.8718   |
| 0.2993        | 25.8462 | 42   | 0.4170          | 0.8718   |
| 0.2993        | 26.4615 | 43   | 0.4171          | 0.8718   |
| 0.2993        | 27.6923 | 45   | 0.4176          | 0.8718   |
| 0.2993        | 28.9231 | 47   | 0.4179          | 0.8718   |
| 0.2993        | 29.5385 | 48   | 0.4179          | 0.8718   |
| 0.298         | 30.7692 | 50   | 0.4179          | 0.8718   |
| 0.298         | 32.0    | 52   | 0.4179          | 0.8718   |
| 0.298         | 32.6154 | 53   | 0.4179          | 0.8718   |
| 0.298         | 33.8462 | 55   | 0.4179          | 0.8718   |
| 0.298         | 34.4615 | 56   | 0.4179          | 0.8718   |
| 0.298         | 35.6923 | 58   | 0.4179          | 0.8718   |
| 0.2936        | 36.9231 | 60   | 0.4178          | 0.8718   |
| 0.2936        | 37.5385 | 61   | 0.4178          | 0.8718   |
| 0.2936        | 38.7692 | 63   | 0.4178          | 0.8718   |
| 0.2936        | 40.0    | 65   | 0.4178          | 0.8718   |
| 0.2936        | 40.6154 | 66   | 0.4178          | 0.8718   |
| 0.2936        | 41.8462 | 68   | 0.4178          | 0.8718   |
| 0.2936        | 42.4615 | 69   | 0.4177          | 0.8718   |
| 0.2948        | 43.6923 | 71   | 0.4177          | 0.8718   |
| 0.2948        | 44.9231 | 73   | 0.4177          | 0.8718   |
| 0.2948        | 45.5385 | 74   | 0.4176          | 0.8718   |
| 0.2948        | 46.7692 | 76   | 0.4176          | 0.8718   |
| 0.2948        | 48.0    | 78   | 0.4176          | 0.8718   |
| 0.2948        | 48.6154 | 79   | 0.4176          | 0.8718   |
| 0.2965        | 49.8462 | 81   | 0.4176          | 0.8718   |
| 0.2965        | 50.4615 | 82   | 0.4175          | 0.8718   |
| 0.2965        | 51.6923 | 84   | 0.4175          | 0.8718   |
| 0.2965        | 52.9231 | 86   | 0.4175          | 0.8718   |
| 0.2965        | 53.5385 | 87   | 0.4175          | 0.8718   |
| 0.2965        | 54.7692 | 89   | 0.4174          | 0.8718   |
| 0.292         | 56.0    | 91   | 0.4174          | 0.8718   |
| 0.292         | 56.6154 | 92   | 0.4174          | 0.8718   |
| 0.292         | 57.8462 | 94   | 0.4174          | 0.8718   |
| 0.292         | 58.4615 | 95   | 0.4173          | 0.8718   |
| 0.292         | 59.6923 | 97   | 0.4173          | 0.8718   |
| 0.292         | 60.9231 | 99   | 0.4173          | 0.8718   |
| 0.2962        | 61.5385 | 100  | 0.4173          | 0.8718   |


### Framework versions

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