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---
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: 0.9814814814814815
---
<!-- 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: 0.1169
- Accuracy: 0.9815
## 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 | 0.8 | 3 | 1.7556 | 0.2037 |
| No log | 1.8 | 6 | 1.5833 | 0.3704 |
| No log | 2.8 | 9 | 1.3483 | 0.5926 |
| No log | 3.8 | 12 | 1.1101 | 0.6667 |
| No log | 4.8 | 15 | 0.9116 | 0.7222 |
| No log | 5.8 | 18 | 0.7632 | 0.7407 |
| 1.7322 | 6.8 | 21 | 0.6118 | 0.7963 |
| 1.7322 | 7.8 | 24 | 0.5017 | 0.8519 |
| 1.7322 | 8.8 | 27 | 0.4241 | 0.8889 |
| 1.7322 | 9.8 | 30 | 0.3522 | 0.8704 |
| 1.7322 | 10.8 | 33 | 0.2918 | 0.9259 |
| 1.7322 | 11.8 | 36 | 0.2659 | 0.9259 |
| 1.7322 | 12.8 | 39 | 0.2587 | 0.9444 |
| 0.7462 | 13.8 | 42 | 0.2063 | 0.9259 |
| 0.7462 | 14.8 | 45 | 0.1870 | 0.9259 |
| 0.7462 | 15.8 | 48 | 0.1739 | 0.9630 |
| 0.7462 | 16.8 | 51 | 0.2043 | 0.9259 |
| 0.7462 | 17.8 | 54 | 0.1897 | 0.9259 |
| 0.7462 | 18.8 | 57 | 0.1764 | 0.9444 |
| 0.4232 | 19.8 | 60 | 0.1587 | 0.9444 |
| 0.4232 | 20.8 | 63 | 0.1556 | 0.9630 |
| 0.4232 | 21.8 | 66 | 0.1516 | 0.9630 |
| 0.4232 | 22.8 | 69 | 0.1264 | 0.9630 |
| 0.4232 | 23.8 | 72 | 0.1180 | 0.9630 |
| 0.4232 | 24.8 | 75 | 0.1110 | 0.9630 |
| 0.4232 | 25.8 | 78 | 0.1232 | 0.9630 |
| 0.3571 | 26.8 | 81 | 0.1169 | 0.9815 |
| 0.3571 | 27.8 | 84 | 0.1051 | 0.9815 |
| 0.3571 | 28.8 | 87 | 0.0986 | 0.9630 |
| 0.3571 | 29.8 | 90 | 0.0937 | 0.9630 |
| 0.3571 | 30.8 | 93 | 0.0931 | 0.9630 |
| 0.3571 | 31.8 | 96 | 0.0932 | 0.9630 |
| 0.3571 | 32.8 | 99 | 0.0941 | 0.9630 |
| 0.3239 | 33.8 | 102 | 0.0920 | 0.9630 |
| 0.3239 | 34.8 | 105 | 0.0851 | 0.9630 |
| 0.3239 | 35.8 | 108 | 0.0828 | 0.9630 |
| 0.3239 | 36.8 | 111 | 0.0810 | 0.9630 |
| 0.3239 | 37.8 | 114 | 0.0801 | 0.9630 |
| 0.3239 | 38.8 | 117 | 0.0804 | 0.9630 |
| 0.3111 | 39.8 | 120 | 0.0807 | 0.9630 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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