transformers tensorboard safetensors swin image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/swin-tiny-patch4-window7-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
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-sealv1
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.946969696969697
---

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# swin-tiny-patch4-window7-224-finetuned-sealv1

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: 0.4069
- Accuracy: 0.9470

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.95  | 9    | 1.1667          | 0.6023   |
| 1.2777        | 2.0   | 19   | 0.9542          | 0.8864   |
| 0.8743        | 2.95  | 28   | 0.5694          | 0.9015   |
| 0.5282        | 4.0   | 38   | 0.3682          | 0.9129   |
| 0.2988        | 4.95  | 47   | 0.2135          | 0.9545   |
| 0.1832        | 6.0   | 57   | 0.2820          | 0.9167   |
| 0.1867        | 6.95  | 66   | 0.1944          | 0.9432   |
| 0.1077        | 8.0   | 76   | 0.2345          | 0.9432   |
| 0.0571        | 8.95  | 85   | 0.2389          | 0.9470   |
| 0.0379        | 10.0  | 95   | 0.2260          | 0.9432   |
| 0.0233        | 10.95 | 104  | 0.2329          | 0.9432   |
| 0.0163        | 12.0  | 114  | 0.2610          | 0.9356   |
| 0.019         | 12.95 | 123  | 0.3660          | 0.9508   |
| 0.0113        | 14.0  | 133  | 0.2777          | 0.9470   |
| 0.0084        | 14.95 | 142  | 0.3123          | 0.9508   |
| 0.008         | 16.0  | 152  | 0.3222          | 0.9470   |
| 0.0048        | 16.95 | 161  | 0.3232          | 0.9470   |
| 0.0075        | 18.0  | 171  | 0.3476          | 0.9508   |
| 0.0048        | 18.95 | 180  | 0.3304          | 0.9470   |
| 0.0143        | 20.0  | 190  | 0.4560          | 0.9432   |
| 0.0143        | 20.95 | 199  | 0.3720          | 0.9432   |
| 0.0019        | 22.0  | 209  | 0.3579          | 0.9394   |
| 0.0063        | 22.95 | 218  | 0.4064          | 0.9432   |
| 0.0023        | 24.0  | 228  | 0.4741          | 0.9394   |
| 0.0015        | 24.95 | 237  | 0.4111          | 0.9470   |
| 0.0022        | 26.0  | 247  | 0.3914          | 0.9432   |
| 0.0008        | 26.95 | 256  | 0.3945          | 0.9432   |
| 0.0024        | 28.0  | 266  | 0.4053          | 0.9470   |
| 0.0026        | 28.42 | 270  | 0.4069          | 0.9470   |


### Framework versions

- Transformers 4.38.2
- Pytorch 1.10.2+cu113
- Datasets 2.18.0
- Tokenizers 0.15.2
