---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: segformer-class-classWeights-augmentation
  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: 0.9655172413793104
---

<!-- 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. -->

# segformer-class-classWeights-augmentation

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: 0.0355
- Accuracy: 0.9655

## 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: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 40
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.89  | 6    | 1.0977          | 0.5517   |
| 1.0215        | 1.93  | 13   | 0.6858          | 0.7931   |
| 0.6364        | 2.96  | 20   | 0.9383          | 0.6897   |
| 0.6364        | 4.0   | 27   | 0.2391          | 0.9310   |
| 0.2716        | 4.89  | 33   | 0.1767          | 0.8966   |
| 0.2295        | 5.93  | 40   | 0.2729          | 0.9310   |
| 0.2295        | 6.96  | 47   | 0.1429          | 0.9655   |
| 0.1311        | 8.0   | 54   | 0.1929          | 0.9655   |
| 0.1503        | 8.89  | 60   | 0.1718          | 0.9655   |
| 0.1503        | 9.93  | 67   | 0.1631          | 0.9655   |
| 0.1554        | 10.96 | 74   | 0.2690          | 0.9655   |
| 0.1157        | 12.0  | 81   | 0.1331          | 0.9655   |
| 0.1157        | 12.89 | 87   | 0.0512          | 0.9655   |
| 0.1093        | 13.93 | 94   | 0.0273          | 1.0      |
| 0.134         | 14.96 | 101  | 0.0356          | 0.9655   |
| 0.134         | 16.0  | 108  | 0.0477          | 0.9655   |
| 0.0926        | 16.89 | 114  | 0.0381          | 0.9655   |
| 0.1363        | 17.78 | 120  | 0.0355          | 0.9655   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3