| --- |
| license: apache-2.0 |
| tags: |
| - image-classification |
| - vision |
| - generated_from_trainer |
| datasets: |
| - gtsrb |
| metrics: |
| - accuracy |
| model-index: |
| - name: gtsrb-model |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: bazyl/GTSRB |
| type: gtsrb |
| args: gtsrb |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.9993199591975519 |
| --- |
| |
| <!-- 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. --> |
|
|
| # gtsrb-model |
|
|
| This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the bazyl/GTSRB dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0034 |
| - Accuracy: 0.9993 |
|
|
| ## Model description |
|
|
| The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge. Our benchmark has the following properties: |
|
|
| - Single-image, multi-class classification problem |
| - More than 40 classes |
| - More than 50,000 images in total |
| - Large, lifelike database |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 1337 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10.0 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:| |
| | 0.2593 | 1.0 | 4166 | 0.1585 | 0.9697 | |
| | 0.2659 | 2.0 | 8332 | 0.0472 | 0.9900 | |
| | 0.2825 | 3.0 | 12498 | 0.0155 | 0.9971 | |
| | 0.0953 | 4.0 | 16664 | 0.0113 | 0.9983 | |
| | 0.1277 | 5.0 | 20830 | 0.0076 | 0.9985 | |
| | 0.0816 | 6.0 | 24996 | 0.0047 | 0.9988 | |
| | 0.0382 | 7.0 | 29162 | 0.0041 | 0.9990 | |
| | 0.0983 | 8.0 | 33328 | 0.0059 | 0.9990 | |
| | 0.1746 | 9.0 | 37494 | 0.0034 | 0.9993 | |
| | 0.1153 | 10.0 | 41660 | 0.0038 | 0.9990 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.21.0.dev0 |
| - Pytorch 1.12.0 |
| - Datasets 2.3.2 |
| - Tokenizers 0.12.1 |
|
|
