---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
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
    - name: F1
      type: f1
      value: 0.964683592269799
    - name: Precision
      type: precision
      value: 0.9674329501915708
    - name: Recall
      type: recall
      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: 0.1855
- Accuracy: 0.9655
- F1: 0.9647
- Precision: 0.9674
- Recall: 0.9655
- Learning Rate: 0.0000

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Rate   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| No log        | 0.89  | 6    | 0.1113          | 0.9655   | 0.9647 | 0.9674    | 0.9655 | 0.0000 |
| 0.1153        | 1.93  | 13   | 0.0929          | 0.9655   | 0.9647 | 0.9674    | 0.9655 | 0.0000 |
| 0.2246        | 2.96  | 20   | 0.1026          | 0.9655   | 0.9647 | 0.9674    | 0.9655 | 0.0000 |
| 0.2246        | 4.0   | 27   | 0.0391          | 0.9655   | 0.9647 | 0.9674    | 0.9655 | 0.0000 |
| 0.1433        | 4.89  | 33   | 0.0673          | 0.9655   | 0.9647 | 0.9674    | 0.9655 | 0.0000 |
| 0.1816        | 5.93  | 40   | 0.0794          | 0.9655   | 0.9647 | 0.9674    | 0.9655 | 0.0000 |
| 0.1816        | 6.96  | 47   | 0.0687          | 0.9655   | 0.9647 | 0.9674    | 0.9655 | 0.0000 |
| 0.1448        | 8.0   | 54   | 0.1123          | 0.9655   | 0.9647 | 0.9674    | 0.9655 | 0.0000 |
| 0.1124        | 8.89  | 60   | 0.1855          | 0.9655   | 0.9647 | 0.9674    | 0.9655 | 0.0000 |


### Framework versions

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