---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: delivery_truck_classification
  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: 0.96875
---

<!-- 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. -->

# delivery_truck_classification

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: 0.1835
- Accuracy: 0.9688

## 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: 60

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.89  | 4    | 2.0074          | 0.1562   |
| No log        | 1.89  | 8    | 1.8896          | 0.25     |
| No log        | 2.89  | 12   | 1.7421          | 0.4062   |
| No log        | 3.89  | 16   | 1.5892          | 0.4375   |
| 1.973         | 4.89  | 20   | 1.3623          | 0.6094   |
| 1.973         | 5.89  | 24   | 1.1093          | 0.6094   |
| 1.973         | 6.89  | 28   | 0.7901          | 0.7812   |
| 1.973         | 7.89  | 32   | 0.5773          | 0.8438   |
| 1.973         | 8.89  | 36   | 0.3857          | 0.8906   |
| 1.0433        | 9.89  | 40   | 0.3254          | 0.9062   |
| 1.0433        | 10.89 | 44   | 0.2461          | 0.9219   |
| 1.0433        | 11.89 | 48   | 0.2340          | 0.9219   |
| 1.0433        | 12.89 | 52   | 0.1835          | 0.9688   |
| 1.0433        | 13.89 | 56   | 0.1779          | 0.9375   |
| 0.5842        | 14.89 | 60   | 0.1545          | 0.9531   |
| 0.5842        | 15.89 | 64   | 0.1487          | 0.9531   |
| 0.5842        | 16.89 | 68   | 0.1996          | 0.9219   |
| 0.5842        | 17.89 | 72   | 0.1619          | 0.9062   |
| 0.5842        | 18.89 | 76   | 0.1350          | 0.9688   |
| 0.4616        | 19.89 | 80   | 0.1706          | 0.9375   |
| 0.4616        | 20.89 | 84   | 0.1579          | 0.9219   |
| 0.4616        | 21.89 | 88   | 0.1630          | 0.9375   |
| 0.4616        | 22.89 | 92   | 0.2080          | 0.9062   |
| 0.4616        | 23.89 | 96   | 0.1463          | 0.9375   |
| 0.3898        | 24.89 | 100  | 0.1185          | 0.9688   |
| 0.3898        | 25.89 | 104  | 0.1445          | 0.9219   |
| 0.3898        | 26.89 | 108  | 0.2051          | 0.9219   |
| 0.3898        | 27.89 | 112  | 0.1928          | 0.9375   |
| 0.3898        | 28.89 | 116  | 0.1365          | 0.9375   |
| 0.3511        | 29.89 | 120  | 0.1057          | 0.9531   |
| 0.3511        | 30.89 | 124  | 0.1091          | 0.9531   |
| 0.3511        | 31.89 | 128  | 0.1894          | 0.9375   |
| 0.3511        | 32.89 | 132  | 0.1208          | 0.9531   |
| 0.3511        | 33.89 | 136  | 0.1101          | 0.9688   |
| 0.3286        | 34.89 | 140  | 0.1409          | 0.9375   |
| 0.3286        | 35.89 | 144  | 0.1830          | 0.9219   |
| 0.3286        | 36.89 | 148  | 0.1519          | 0.9219   |
| 0.3286        | 37.89 | 152  | 0.1031          | 0.9531   |
| 0.3286        | 38.89 | 156  | 0.0962          | 0.9688   |
| 0.3095        | 39.89 | 160  | 0.0903          | 0.9688   |
| 0.3095        | 40.89 | 164  | 0.0886          | 0.9688   |
| 0.3095        | 41.89 | 168  | 0.1033          | 0.9688   |
| 0.3095        | 42.89 | 172  | 0.1117          | 0.9531   |
| 0.3095        | 43.89 | 176  | 0.1192          | 0.9375   |
| 0.3056        | 44.89 | 180  | 0.0984          | 0.9531   |
| 0.3056        | 45.89 | 184  | 0.0820          | 0.9531   |
| 0.3056        | 46.89 | 188  | 0.0857          | 0.9531   |
| 0.3056        | 47.89 | 192  | 0.1058          | 0.9531   |
| 0.3056        | 48.89 | 196  | 0.1163          | 0.9375   |
| 0.255         | 49.89 | 200  | 0.1121          | 0.9531   |
| 0.255         | 50.89 | 204  | 0.1004          | 0.9688   |
| 0.255         | 51.89 | 208  | 0.0954          | 0.9688   |
| 0.255         | 52.89 | 212  | 0.0925          | 0.9688   |
| 0.255         | 53.89 | 216  | 0.0892          | 0.9688   |
| 0.2494        | 54.89 | 220  | 0.0893          | 0.9688   |
| 0.2494        | 55.89 | 224  | 0.0901          | 0.9688   |
| 0.2494        | 56.89 | 228  | 0.0896          | 0.9688   |
| 0.2494        | 57.89 | 232  | 0.0903          | 0.9688   |
| 0.2494        | 58.89 | 236  | 0.0913          | 0.9688   |
| 0.2588        | 59.89 | 240  | 0.0918          | 0.9688   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2