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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: 0.8571428571428571
---
<!-- 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: 0.7036
- Accuracy: 0.8571
## 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 | 1.9875 | 0.1429 |
| No log | 2.0 | 2 | 1.9132 | 0.1429 |
| No log | 3.0 | 3 | 1.7585 | 0.4286 |
| No log | 4.0 | 4 | 1.5935 | 0.4286 |
| No log | 5.0 | 5 | 1.5026 | 0.4286 |
| No log | 6.0 | 6 | 1.4699 | 0.4286 |
| No log | 7.0 | 7 | 1.4361 | 0.4286 |
| No log | 8.0 | 8 | 1.3962 | 0.4286 |
| No log | 9.0 | 9 | 1.3457 | 0.4286 |
| No log | 10.0 | 10 | 1.2874 | 0.4286 |
| No log | 11.0 | 11 | 1.2240 | 0.4286 |
| No log | 12.0 | 12 | 1.1643 | 0.4286 |
| No log | 13.0 | 13 | 1.1016 | 0.5714 |
| No log | 14.0 | 14 | 1.0356 | 0.5714 |
| No log | 15.0 | 15 | 0.9719 | 0.7143 |
| No log | 16.0 | 16 | 0.9120 | 0.7143 |
| No log | 17.0 | 17 | 0.8606 | 0.7143 |
| No log | 18.0 | 18 | 0.8117 | 0.7143 |
| No log | 19.0 | 19 | 0.7707 | 0.7143 |
| 0.5111 | 20.0 | 20 | 0.7367 | 0.7143 |
| 0.5111 | 21.0 | 21 | 0.7157 | 0.7143 |
| 0.5111 | 22.0 | 22 | 0.7067 | 0.7143 |
| 0.5111 | 23.0 | 23 | 0.7012 | 0.7143 |
| 0.5111 | 24.0 | 24 | 0.6977 | 0.7143 |
| 0.5111 | 25.0 | 25 | 0.6974 | 0.7143 |
| 0.5111 | 26.0 | 26 | 0.6977 | 0.7143 |
| 0.5111 | 27.0 | 27 | 0.7036 | 0.8571 |
| 0.5111 | 28.0 | 28 | 0.7074 | 0.8571 |
| 0.5111 | 29.0 | 29 | 0.7062 | 0.8571 |
| 0.5111 | 30.0 | 30 | 0.7056 | 0.8571 |
| 0.5111 | 31.0 | 31 | 0.7050 | 0.8571 |
| 0.5111 | 32.0 | 32 | 0.7050 | 0.8571 |
| 0.5111 | 33.0 | 33 | 0.7031 | 0.8571 |
| 0.5111 | 34.0 | 34 | 0.7016 | 0.8571 |
| 0.5111 | 35.0 | 35 | 0.6996 | 0.8571 |
| 0.5111 | 36.0 | 36 | 0.6971 | 0.8571 |
| 0.5111 | 37.0 | 37 | 0.6953 | 0.8571 |
| 0.5111 | 38.0 | 38 | 0.6939 | 0.8571 |
| 0.5111 | 39.0 | 39 | 0.6938 | 0.8571 |
| 0.1719 | 40.0 | 40 | 0.6936 | 0.8571 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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