---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-finalterm
  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: 0.9
---

<!-- 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. -->

# swinv2-tiny-patch4-window8-256-finalterm

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2805
- Accuracy: 0.9

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3578        | 1.0   | 10   | 1.2444          | 0.475    |
| 1.1054        | 2.0   | 20   | 0.9180          | 0.6531   |
| 0.8485        | 3.0   | 30   | 0.6632          | 0.725    |
| 0.674         | 4.0   | 40   | 0.4736          | 0.7969   |
| 0.5968        | 5.0   | 50   | 0.4341          | 0.8125   |
| 0.508         | 6.0   | 60   | 0.5391          | 0.8187   |
| 0.4852        | 7.0   | 70   | 0.3906          | 0.8344   |
| 0.4354        | 8.0   | 80   | 0.3257          | 0.8656   |
| 0.4165        | 9.0   | 90   | 0.3478          | 0.8656   |
| 0.4385        | 10.0  | 100  | 0.3114          | 0.8781   |
| 0.4156        | 11.0  | 110  | 0.3461          | 0.8781   |
| 0.4055        | 12.0  | 120  | 0.3108          | 0.8844   |
| 0.4282        | 13.0  | 130  | 0.2916          | 0.8875   |
| 0.3546        | 14.0  | 140  | 0.2972          | 0.9      |
| 0.3608        | 15.0  | 150  | 0.3428          | 0.8688   |
| 0.369         | 16.0  | 160  | 0.2885          | 0.8969   |
| 0.3525        | 17.0  | 170  | 0.2861          | 0.9      |
| 0.338         | 18.0  | 180  | 0.2832          | 0.9062   |
| 0.3633        | 19.0  | 190  | 0.2797          | 0.9031   |
| 0.3712        | 20.0  | 200  | 0.2805          | 0.9      |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1