---
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: 0.9714285714285714
---

<!-- 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: 0.1192
- Accuracy: 0.9714

## 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        | 1.0   | 5    | 1.9402          | 0.1286   |
| No log        | 2.0   | 10   | 1.8379          | 0.2429   |
| No log        | 3.0   | 15   | 1.6960          | 0.4      |
| 1.7795        | 4.0   | 20   | 1.4423          | 0.5143   |
| 1.7795        | 5.0   | 25   | 1.1295          | 0.6857   |
| 1.7795        | 6.0   | 30   | 0.8280          | 0.7286   |
| 1.7795        | 7.0   | 35   | 0.5572          | 0.8429   |
| 1.0588        | 8.0   | 40   | 0.3855          | 0.9286   |
| 1.0588        | 9.0   | 45   | 0.3107          | 0.9143   |
| 1.0588        | 10.0  | 50   | 0.2564          | 0.9286   |
| 1.0588        | 11.0  | 55   | 0.2050          | 0.9286   |
| 0.591         | 12.0  | 60   | 0.1900          | 0.9571   |
| 0.591         | 13.0  | 65   | 0.1720          | 0.9286   |
| 0.591         | 14.0  | 70   | 0.1881          | 0.9143   |
| 0.591         | 15.0  | 75   | 0.1789          | 0.9429   |
| 0.4609        | 16.0  | 80   | 0.1999          | 0.9143   |
| 0.4609        | 17.0  | 85   | 0.1492          | 0.9286   |
| 0.4609        | 18.0  | 90   | 0.1648          | 0.9286   |
| 0.4609        | 19.0  | 95   | 0.1195          | 0.9571   |
| 0.3941        | 20.0  | 100  | 0.1395          | 0.9286   |
| 0.3941        | 21.0  | 105  | 0.1476          | 0.9286   |
| 0.3941        | 22.0  | 110  | 0.1113          | 0.9571   |
| 0.3941        | 23.0  | 115  | 0.1328          | 0.9571   |
| 0.3475        | 24.0  | 120  | 0.1192          | 0.9714   |
| 0.3475        | 25.0  | 125  | 0.1200          | 0.9571   |
| 0.3475        | 26.0  | 130  | 0.1360          | 0.9714   |
| 0.3475        | 27.0  | 135  | 0.1425          | 0.9429   |
| 0.3542        | 28.0  | 140  | 0.1103          | 0.9571   |
| 0.3542        | 29.0  | 145  | 0.1244          | 0.9429   |
| 0.3542        | 30.0  | 150  | 0.1176          | 0.9571   |
| 0.3542        | 31.0  | 155  | 0.1028          | 0.9571   |
| 0.317         | 32.0  | 160  | 0.1084          | 0.9571   |
| 0.317         | 33.0  | 165  | 0.1269          | 0.9571   |
| 0.317         | 34.0  | 170  | 0.1295          | 0.9429   |
| 0.317         | 35.0  | 175  | 0.1245          | 0.9571   |
| 0.2947        | 36.0  | 180  | 0.1315          | 0.9429   |
| 0.2947        | 37.0  | 185  | 0.1313          | 0.9571   |
| 0.2947        | 38.0  | 190  | 0.1421          | 0.9429   |
| 0.2947        | 39.0  | 195  | 0.1440          | 0.9571   |
| 0.3124        | 40.0  | 200  | 0.1339          | 0.9571   |
| 0.3124        | 41.0  | 205  | 0.1553          | 0.9429   |
| 0.3124        | 42.0  | 210  | 0.1547          | 0.9429   |
| 0.3124        | 43.0  | 215  | 0.1316          | 0.9571   |
| 0.2843        | 44.0  | 220  | 0.1287          | 0.9571   |
| 0.2843        | 45.0  | 225  | 0.1308          | 0.9571   |
| 0.2843        | 46.0  | 230  | 0.1401          | 0.9571   |
| 0.2843        | 47.0  | 235  | 0.1186          | 0.9571   |
| 0.2655        | 48.0  | 240  | 0.1057          | 0.9571   |
| 0.2655        | 49.0  | 245  | 0.1203          | 0.9571   |
| 0.2655        | 50.0  | 250  | 0.1374          | 0.9571   |
| 0.2655        | 51.0  | 255  | 0.1361          | 0.9571   |
| 0.26          | 52.0  | 260  | 0.1198          | 0.9571   |
| 0.26          | 53.0  | 265  | 0.1175          | 0.9571   |
| 0.26          | 54.0  | 270  | 0.1313          | 0.9571   |
| 0.26          | 55.0  | 275  | 0.1398          | 0.9429   |
| 0.2601        | 56.0  | 280  | 0.1354          | 0.9571   |
| 0.2601        | 57.0  | 285  | 0.1271          | 0.9571   |
| 0.2601        | 58.0  | 290  | 0.1242          | 0.9571   |
| 0.2601        | 59.0  | 295  | 0.1233          | 0.9571   |
| 0.2562        | 60.0  | 300  | 0.1235          | 0.9571   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2