
<!-- 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

{"license": "apache-2.0", "tags": ["image-classification", "vision", "generated_from_trainer"], "datasets": ["gtsrb"], "metrics": ["accuracy"], "model-index": [{"name": "gtsrb-model", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "bazyl/GTSRB", "type": "gtsrb", "args": "gtsrb"}, "metrics": [{"type": "accuracy", "value": 0.9993199591975519, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification vision generated_from_trainer dataset:gtsrb license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #vit #image-classification #vision #generated_from_trainer #dataset-gtsrb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

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



### 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
TAGS
#transformers #pytorch #vit #image-classification #vision #generated_from_trainer #dataset-gtsrb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### 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
passage: TAGS
#transformers #pytorch #vit #image-classification #vision #generated_from_trainer #dataset-gtsrb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### 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