README.md

metadata

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

gtsrb-model

This model is a fine-tuned version of 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