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# Traffic level image classification

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4394
- Accuracy: 0.8292
- Precision: 0.8232
- Recall: 0.7366
- F1: 0.7721

## Model description

Built from 6,000 images fetched from public traffic cameras in Norway to classify traffic levels from low, medium to high. Dataset is unbalanced skewed towards low traffic images.

## 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: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6282        | 0.9843 | 47   | 0.5725          | 0.7644   | 0.7933    | 0.5918 | 0.6525 |
| 0.4486        | 1.9895 | 95   | 0.4630          | 0.8012   | 0.7964    | 0.6824 | 0.7213 |
| 0.3285        | 2.9948 | 143  | 0.4394          | 0.8292   | 0.8232    | 0.7366 | 0.7721 |
| 0.2391        | 4.0    | 191  | 0.4302          | 0.8115   | 0.7941    | 0.7333 | 0.7555 |
| 0.1814        | 4.9215 | 235  | 0.4365          | 0.8218   | 0.7993    | 0.7362 | 0.7631 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1

high-traffic low-traffic medium-traffic no-traffic