---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-fish
  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: 0.6666666666666666
---

<!-- 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. -->

# swin-tiny-patch4-window7-224-finetuned-fish

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8536
- Accuracy: 0.6667

## 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: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 1    | 1.9028          | 0.3333   |
| No log        | 2.0   | 2    | 1.8803          | 0.3333   |
| No log        | 3.0   | 3    | 1.8603          | 0.3333   |
| No log        | 4.0   | 4    | 1.8293          | 0.3333   |
| No log        | 5.0   | 5    | 1.8093          | 0.3333   |
| No log        | 6.0   | 6    | 1.7682          | 0.3333   |
| No log        | 7.0   | 7    | 1.7140          | 0.3333   |
| No log        | 8.0   | 8    | 1.6566          | 0.3333   |
| No log        | 9.0   | 9    | 1.6020          | 0.3333   |
| 0.8416        | 10.0  | 10   | 1.5466          | 0.3333   |
| 0.8416        | 11.0  | 11   | 1.4709          | 0.3333   |
| 0.8416        | 12.0  | 12   | 1.3894          | 0.3333   |
| 0.8416        | 13.0  | 13   | 1.3049          | 0.5      |
| 0.8416        | 14.0  | 14   | 1.2186          | 0.6667   |
| 0.8416        | 15.0  | 15   | 1.1344          | 0.6667   |
| 0.8416        | 16.0  | 16   | 1.0706          | 0.6667   |
| 0.8416        | 17.0  | 17   | 1.0184          | 0.6667   |
| 0.8416        | 18.0  | 18   | 0.9807          | 0.6667   |
| 0.8416        | 19.0  | 19   | 0.9455          | 0.6667   |
| 0.433         | 20.0  | 20   | 0.9182          | 0.6667   |
| 0.433         | 21.0  | 21   | 0.8942          | 0.6667   |
| 0.433         | 22.0  | 22   | 0.8766          | 0.6667   |
| 0.433         | 23.0  | 23   | 0.8654          | 0.6667   |
| 0.433         | 24.0  | 24   | 0.8578          | 0.6667   |
| 0.433         | 25.0  | 25   | 0.8536          | 0.6667   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1