---
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: 0.2027
- 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        | 1.0   | 1    | 1.6169          | 0.4286   |
| No log        | 2.0   | 2    | 1.5622          | 0.5      |
| No log        | 3.0   | 3    | 1.4656          | 0.5714   |
| No log        | 4.0   | 4    | 1.3434          | 0.7143   |
| No log        | 5.0   | 5    | 1.1958          | 0.8571   |
| No log        | 6.0   | 6    | 1.0398          | 0.8571   |
| No log        | 7.0   | 7    | 0.8839          | 0.8571   |
| No log        | 8.0   | 8    | 0.7458          | 0.8571   |
| No log        | 9.0   | 9    | 0.6267          | 0.8571   |
| No log        | 10.0  | 10   | 0.5253          | 0.8571   |
| No log        | 11.0  | 11   | 0.4414          | 0.8571   |
| No log        | 12.0  | 12   | 0.3764          | 0.8571   |
| No log        | 13.0  | 13   | 0.3250          | 0.8571   |
| No log        | 14.0  | 14   | 0.2810          | 0.8571   |
| No log        | 15.0  | 15   | 0.2406          | 0.9286   |
| No log        | 16.0  | 16   | 0.2027          | 1.0      |
| No log        | 17.0  | 17   | 0.1679          | 1.0      |
| No log        | 18.0  | 18   | 0.1376          | 1.0      |
| No log        | 19.0  | 19   | 0.1119          | 1.0      |
| 1.0444        | 20.0  | 20   | 0.0910          | 1.0      |
| 1.0444        | 21.0  | 21   | 0.0734          | 1.0      |
| 1.0444        | 22.0  | 22   | 0.0616          | 1.0      |
| 1.0444        | 23.0  | 23   | 0.0536          | 1.0      |
| 1.0444        | 24.0  | 24   | 0.0478          | 1.0      |
| 1.0444        | 25.0  | 25   | 0.0437          | 1.0      |
| 1.0444        | 26.0  | 26   | 0.0414          | 1.0      |
| 1.0444        | 27.0  | 27   | 0.0376          | 1.0      |
| 1.0444        | 28.0  | 28   | 0.0342          | 1.0      |
| 1.0444        | 29.0  | 29   | 0.0313          | 1.0      |
| 1.0444        | 30.0  | 30   | 0.0287          | 1.0      |
| 1.0444        | 31.0  | 31   | 0.0274          | 1.0      |
| 1.0444        | 32.0  | 32   | 0.0267          | 1.0      |
| 1.0444        | 33.0  | 33   | 0.0263          | 1.0      |
| 1.0444        | 34.0  | 34   | 0.0260          | 1.0      |
| 1.0444        | 35.0  | 35   | 0.0258          | 1.0      |
| 1.0444        | 36.0  | 36   | 0.0255          | 1.0      |
| 1.0444        | 37.0  | 37   | 0.0249          | 1.0      |
| 1.0444        | 38.0  | 38   | 0.0246          | 1.0      |
| 1.0444        | 39.0  | 39   | 0.0243          | 1.0      |
| 0.3497        | 40.0  | 40   | 0.0240          | 1.0      |


### Framework versions

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