gtsrb-model

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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

Framework versions

• Transformers 4.21.0.dev0
• Pytorch 1.12.0
• Datasets 2.3.2
• Tokenizers 0.12.1