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: leaftype-swin-tiny-patch4-window7-224-finetuned
  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: 0.9262899262899262
---

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

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: 0.1713
- Accuracy: 0.9263

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7327        | 0.94  | 12   | 0.6155          | 0.6044   |
| 0.5479        | 1.96  | 25   | 0.4489          | 0.7936   |
| 0.3205        | 2.98  | 38   | 0.2164          | 0.9115   |
| 0.2386        | 4.0   | 51   | 0.2124          | 0.9115   |
| 0.1937        | 4.94  | 63   | 0.2349          | 0.9091   |
| 0.1884        | 5.96  | 76   | 0.1752          | 0.9214   |
| 0.2186        | 6.98  | 89   | 0.3518          | 0.8698   |
| 0.1833        | 8.0   | 102  | 0.2443          | 0.9017   |
| 0.1856        | 8.94  | 114  | 0.2492          | 0.9017   |
| 0.1605        | 9.96  | 127  | 0.2005          | 0.9189   |
| 0.1934        | 10.98 | 140  | 0.1713          | 0.9263   |
| 0.186         | 12.0  | 153  | 0.1573          | 0.9238   |
| 0.1407        | 12.94 | 165  | 0.1658          | 0.9214   |
| 0.1655        | 13.96 | 178  | 0.1570          | 0.9214   |
| 0.1448        | 14.98 | 191  | 0.1622          | 0.9238   |
| 0.1525        | 16.0  | 204  | 0.2110          | 0.9165   |
| 0.1226        | 16.94 | 216  | 0.2109          | 0.9165   |
| 0.1556        | 17.96 | 229  | 0.1914          | 0.9165   |
| 0.1264        | 18.98 | 242  | 0.2251          | 0.9115   |
| 0.1249        | 20.0  | 255  | 0.2863          | 0.8993   |
| 0.1099        | 20.94 | 267  | 0.2447          | 0.9066   |
| 0.1223        | 21.96 | 280  | 0.2358          | 0.9042   |
| 0.1084        | 22.98 | 293  | 0.1713          | 0.9238   |
| 0.1054        | 24.0  | 306  | 0.2085          | 0.9115   |
| 0.1055        | 24.94 | 318  | 0.2002          | 0.9115   |
| 0.1167        | 25.96 | 331  | 0.2289          | 0.9140   |
| 0.1156        | 26.98 | 344  | 0.1889          | 0.9115   |
| 0.0919        | 28.0  | 357  | 0.2015          | 0.9115   |
| 0.0827        | 28.24 | 360  | 0.2021          | 0.9091   |


### Framework versions

- Transformers 4.38.1
- Pytorch 1.10.0+cu111
- Datasets 2.17.1
- Tokenizers 0.15.2
