---
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
---

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# 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