---
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-herbify
  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: 1.0
---

<!-- 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-herbify

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: 0.0378
- Accuracy: 1.0

## 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: 35

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.94  | 4    | 1.8723          | 0.2787   |
| No log        | 1.88  | 8    | 1.5899          | 0.6885   |
| 1.8465        | 2.82  | 12   | 1.1661          | 0.8197   |
| 1.8465        | 4.0   | 17   | 0.5156          | 0.9508   |
| 0.9675        | 4.94  | 21   | 0.2177          | 0.9836   |
| 0.9675        | 5.88  | 25   | 0.0929          | 0.9836   |
| 0.9675        | 6.82  | 29   | 0.0378          | 1.0      |
| 0.2342        | 8.0   | 34   | 0.0128          | 1.0      |
| 0.2342        | 8.94  | 38   | 0.0075          | 1.0      |
| 0.1022        | 9.88  | 42   | 0.0053          | 1.0      |
| 0.1022        | 10.82 | 46   | 0.0049          | 1.0      |
| 0.0553        | 12.0  | 51   | 0.0032          | 1.0      |
| 0.0553        | 12.94 | 55   | 0.0022          | 1.0      |
| 0.0553        | 13.88 | 59   | 0.0017          | 1.0      |
| 0.0278        | 14.82 | 63   | 0.0018          | 1.0      |
| 0.0278        | 16.0  | 68   | 0.0012          | 1.0      |
| 0.0266        | 16.94 | 72   | 0.0011          | 1.0      |
| 0.0266        | 17.88 | 76   | 0.0006          | 1.0      |
| 0.046         | 18.82 | 80   | 0.0007          | 1.0      |
| 0.046         | 20.0  | 85   | 0.0007          | 1.0      |
| 0.046         | 20.94 | 89   | 0.0012          | 1.0      |
| 0.0245        | 21.88 | 93   | 0.0015          | 1.0      |
| 0.0245        | 22.82 | 97   | 0.0011          | 1.0      |
| 0.0249        | 24.0  | 102  | 0.0007          | 1.0      |
| 0.0249        | 24.94 | 106  | 0.0006          | 1.0      |
| 0.0201        | 25.88 | 110  | 0.0005          | 1.0      |
| 0.0201        | 26.82 | 114  | 0.0005          | 1.0      |
| 0.0201        | 28.0  | 119  | 0.0004          | 1.0      |
| 0.0208        | 28.94 | 123  | 0.0004          | 1.0      |
| 0.0208        | 29.88 | 127  | 0.0004          | 1.0      |
| 0.0122        | 30.82 | 131  | 0.0004          | 1.0      |
| 0.0122        | 32.0  | 136  | 0.0004          | 1.0      |
| 0.0222        | 32.94 | 140  | 0.0004          | 1.0      |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cpu
- Datasets 2.14.5
- Tokenizers 0.13.3