Traffic level image classification
This model is a fine-tuned version of 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
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