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

bazyl/gtsrb-model

gtsrb-model

Model description

Training hyperparameters

Training results

Framework versions

bazyl/
gtsrb-model