---
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: 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. -->

# 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: 0.2212
- 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: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.67  | 1    | 1.7282          | 0.3      |
| No log        | 1.67  | 2    | 1.6786          | 0.3      |
| No log        | 2.67  | 3    | 1.5811          | 0.35     |
| No log        | 3.67  | 4    | 1.4410          | 0.45     |
| No log        | 4.67  | 5    | 1.2802          | 0.65     |
| No log        | 5.67  | 6    | 1.1453          | 0.75     |
| No log        | 6.67  | 7    | 1.0253          | 0.75     |
| No log        | 7.67  | 8    | 0.9306          | 0.75     |
| No log        | 8.67  | 9    | 0.8566          | 0.8      |
| No log        | 9.67  | 10   | 0.8048          | 0.8      |
| No log        | 10.67 | 11   | 0.7585          | 0.8      |
| No log        | 11.67 | 12   | 0.7097          | 0.8      |
| No log        | 12.67 | 13   | 0.6443          | 0.8      |
| No log        | 13.67 | 14   | 0.5772          | 0.8      |
| No log        | 14.67 | 15   | 0.5056          | 0.8      |
| No log        | 15.67 | 16   | 0.4444          | 0.8      |
| No log        | 16.67 | 17   | 0.3857          | 0.85     |
| No log        | 17.67 | 18   | 0.3330          | 0.85     |
| No log        | 18.67 | 19   | 0.2907          | 0.9      |
| 1.4985        | 19.67 | 20   | 0.2552          | 0.95     |
| 1.4985        | 20.67 | 21   | 0.2212          | 1.0      |
| 1.4985        | 21.67 | 22   | 0.1938          | 1.0      |
| 1.4985        | 22.67 | 23   | 0.1699          | 1.0      |
| 1.4985        | 23.67 | 24   | 0.1490          | 1.0      |
| 1.4985        | 24.67 | 25   | 0.1329          | 1.0      |
| 1.4985        | 25.67 | 26   | 0.1203          | 1.0      |
| 1.4985        | 26.67 | 27   | 0.1141          | 1.0      |
| 1.4985        | 27.67 | 28   | 0.1084          | 1.0      |
| 1.4985        | 28.67 | 29   | 0.1018          | 1.0      |
| 1.4985        | 29.67 | 30   | 0.0953          | 1.0      |
| 1.4985        | 30.67 | 31   | 0.0878          | 1.0      |
| 1.4985        | 31.67 | 32   | 0.0794          | 1.0      |
| 1.4985        | 32.67 | 33   | 0.0730          | 1.0      |
| 1.4985        | 33.67 | 34   | 0.0687          | 1.0      |
| 1.4985        | 34.67 | 35   | 0.0664          | 1.0      |
| 1.4985        | 35.67 | 36   | 0.0649          | 1.0      |
| 1.4985        | 36.67 | 37   | 0.0640          | 1.0      |
| 1.4985        | 37.67 | 38   | 0.0639          | 1.0      |
| 1.4985        | 38.67 | 39   | 0.0638          | 1.0      |
| 0.4842        | 39.67 | 40   | 0.0637          | 1.0      |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1