---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: 2024_08_16_swinv2-base-patch4-window8-256
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8068181818181818
---

<!-- 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. -->

# 2024_08_16_swinv2-base-patch4-window8-256

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4529
- Accuracy: 0.8068

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6942        | 1.0   | 87   | 0.6796          | 0.6818   |
| 0.6661        | 2.0   | 174  | 0.5973          | 0.7045   |
| 0.5666        | 3.0   | 261  | 0.5844          | 0.7045   |
| 0.6134        | 4.0   | 348  | 0.5798          | 0.7045   |
| 0.5207        | 5.0   | 435  | 0.5817          | 0.6932   |
| 0.75          | 6.0   | 522  | 0.5488          | 0.7159   |
| 0.4155        | 7.0   | 609  | 0.5373          | 0.7045   |
| 0.5122        | 8.0   | 696  | 0.5057          | 0.7386   |
| 0.722         | 9.0   | 783  | 0.4951          | 0.7045   |
| 0.5301        | 10.0  | 870  | 0.5349          | 0.7727   |
| 0.5881        | 11.0  | 957  | 0.4795          | 0.7955   |
| 0.5295        | 12.0  | 1044 | 0.4843          | 0.7955   |
| 0.6252        | 13.0  | 1131 | 0.4529          | 0.8068   |
| 0.9347        | 14.0  | 1218 | 0.4670          | 0.7955   |
| 0.5375        | 15.0  | 1305 | 0.4468          | 0.8068   |
| 0.4811        | 16.0  | 1392 | 0.4912          | 0.7841   |
| 0.5728        | 17.0  | 1479 | 0.4636          | 0.8068   |
| 0.7997        | 18.0  | 1566 | 0.4631          | 0.8068   |
| 0.4473        | 19.0  | 1653 | 0.4785          | 0.8068   |
| 0.4999        | 20.0  | 1740 | 0.5162          | 0.8068   |
| 0.4572        | 21.0  | 1827 | 0.5742          | 0.7955   |
| 0.2571        | 22.0  | 1914 | 0.5181          | 0.7955   |
| 0.5085        | 23.0  | 2001 | 0.4937          | 0.7955   |
| 0.7698        | 24.0  | 2088 | 0.4764          | 0.7955   |
| 0.558         | 25.0  | 2175 | 0.4742          | 0.8068   |
| 0.5462        | 26.0  | 2262 | 0.5320          | 0.7841   |
| 0.5218        | 27.0  | 2349 | 0.5298          | 0.7841   |
| 0.5228        | 28.0  | 2436 | 0.5182          | 0.7955   |
| 0.5787        | 29.0  | 2523 | 0.5104          | 0.8068   |
| 0.7511        | 30.0  | 2610 | 0.5152          | 0.8068   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1