---
library_name: transformers
base_model: Melo1512/vit-msn-small-beta-fia-manually-enhanced-HSV_test_2
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-msn-small-beta-fia-manually-enhanced-HSV_test_3
  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: 0.8802816901408451
---

<!-- 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-beta-fia-manually-enhanced-HSV_test_3

This model is a fine-tuned version of [Melo1512/vit-msn-small-beta-fia-manually-enhanced-HSV_test_2](https://huggingface.co/Melo1512/vit-msn-small-beta-fia-manually-enhanced-HSV_test_2) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5013
- Accuracy: 0.8803

## 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: 1e-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: 0.15
- num_epochs: 50
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.5714  | 1    | 0.5123          | 0.8873   |
| No log        | 1.7143  | 3    | 0.5219          | 0.8873   |
| No log        | 2.8571  | 5    | 0.5431          | 0.8732   |
| No log        | 4.0     | 7    | 0.5444          | 0.8732   |
| No log        | 4.5714  | 8    | 0.5336          | 0.8803   |
| 0.4252        | 5.7143  | 10   | 0.5235          | 0.8873   |
| 0.4252        | 6.8571  | 12   | 0.5269          | 0.8803   |
| 0.4252        | 8.0     | 14   | 0.5106          | 0.8873   |
| 0.4252        | 8.5714  | 15   | 0.5048          | 0.8873   |
| 0.4252        | 9.7143  | 17   | 0.5013          | 0.8803   |
| 0.4252        | 10.8571 | 19   | 0.5105          | 0.8803   |
| 0.4413        | 12.0    | 21   | 0.5256          | 0.8803   |
| 0.4413        | 12.5714 | 22   | 0.5303          | 0.8732   |
| 0.4413        | 13.7143 | 24   | 0.5218          | 0.8662   |
| 0.4413        | 14.8571 | 26   | 0.5188          | 0.8592   |
| 0.4413        | 16.0    | 28   | 0.5202          | 0.8592   |
| 0.4413        | 16.5714 | 29   | 0.5252          | 0.8592   |
| 0.437         | 17.7143 | 31   | 0.5385          | 0.8592   |
| 0.437         | 18.8571 | 33   | 0.5456          | 0.8592   |
| 0.437         | 20.0    | 35   | 0.5409          | 0.8732   |
| 0.437         | 20.5714 | 36   | 0.5375          | 0.8662   |
| 0.437         | 21.7143 | 38   | 0.5356          | 0.8662   |
| 0.4343        | 22.8571 | 40   | 0.5328          | 0.8803   |
| 0.4343        | 24.0    | 42   | 0.5318          | 0.8803   |
| 0.4343        | 24.5714 | 43   | 0.5330          | 0.8803   |
| 0.4343        | 25.7143 | 45   | 0.5334          | 0.8803   |
| 0.4343        | 26.8571 | 47   | 0.5332          | 0.8732   |
| 0.4343        | 28.0    | 49   | 0.5341          | 0.8732   |
| 0.4271        | 28.5714 | 50   | 0.5343          | 0.8732   |


### Framework versions

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