---
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: 0.8732394366197183
---

<!-- 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: 0.5558
- Accuracy: 0.8732

## 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    | 0.5495          | 0.8662   |
| No log        | 2.8571  | 5    | 0.6080          | 0.8592   |
| No log        | 4.0     | 7    | 0.5591          | 0.8732   |
| No log        | 4.5714  | 8    | 0.5464          | 0.8732   |
| 0.4241        | 5.7143  | 10   | 0.5982          | 0.8451   |
| 0.4241        | 6.8571  | 12   | 0.6497          | 0.8169   |
| 0.4241        | 8.0     | 14   | 0.5928          | 0.8521   |
| 0.4241        | 8.5714  | 15   | 0.5711          | 0.8521   |
| 0.4241        | 9.7143  | 17   | 0.5468          | 0.8732   |
| 0.4241        | 10.8571 | 19   | 0.5483          | 0.8521   |
| 0.4152        | 12.0    | 21   | 0.5783          | 0.8451   |
| 0.4152        | 12.5714 | 22   | 0.5835          | 0.8451   |
| 0.4152        | 13.7143 | 24   | 0.5668          | 0.8451   |
| 0.4152        | 14.8571 | 26   | 0.5556          | 0.8451   |
| 0.4152        | 16.0    | 28   | 0.5564          | 0.8451   |
| 0.4152        | 16.5714 | 29   | 0.5591          | 0.8451   |
| 0.4367        | 17.7143 | 31   | 0.5619          | 0.8592   |
| 0.4367        | 18.8571 | 33   | 0.5809          | 0.8592   |
| 0.4367        | 20.0    | 35   | 0.5810          | 0.8662   |
| 0.4367        | 20.5714 | 36   | 0.5768          | 0.8662   |
| 0.4367        | 21.7143 | 38   | 0.5591          | 0.8732   |
| 0.4241        | 22.8571 | 40   | 0.5452          | 0.8732   |
| 0.4241        | 24.0    | 42   | 0.5387          | 0.8732   |
| 0.4241        | 24.5714 | 43   | 0.5398          | 0.8732   |
| 0.4241        | 25.7143 | 45   | 0.5458          | 0.8732   |
| 0.4241        | 26.8571 | 47   | 0.5509          | 0.8732   |
| 0.4241        | 28.0    | 49   | 0.5550          | 0.8732   |
| 0.4171        | 28.5714 | 50   | 0.5558          | 0.8732   |


### Framework versions

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