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--- |
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license: apache-2.0 |
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tags: |
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- image-classification |
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- vision |
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- generated_from_trainer |
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datasets: |
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- gtsrb |
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metrics: |
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- accuracy |
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model-index: |
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- name: gtsrb-model |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: bazyl/GTSRB |
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type: gtsrb |
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args: gtsrb |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9993199591975519 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# gtsrb-model |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0034 |
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- Accuracy: 0.9993 |
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## Model description |
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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: |
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- Single-image, multi-class classification problem |
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- More than 40 classes |
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- More than 50,000 images in total |
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- Large, lifelike database |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 1337 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.2593 | 1.0 | 4166 | 0.1585 | 0.9697 | |
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| 0.2659 | 2.0 | 8332 | 0.0472 | 0.9900 | |
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| 0.2825 | 3.0 | 12498 | 0.0155 | 0.9971 | |
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| 0.0953 | 4.0 | 16664 | 0.0113 | 0.9983 | |
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| 0.1277 | 5.0 | 20830 | 0.0076 | 0.9985 | |
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| 0.0816 | 6.0 | 24996 | 0.0047 | 0.9988 | |
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| 0.0382 | 7.0 | 29162 | 0.0041 | 0.9990 | |
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| 0.0983 | 8.0 | 33328 | 0.0059 | 0.9990 | |
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| 0.1746 | 9.0 | 37494 | 0.0034 | 0.9993 | |
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| 0.1153 | 10.0 | 41660 | 0.0038 | 0.9990 | |
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### Framework versions |
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- Transformers 4.21.0.dev0 |
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- Pytorch 1.12.0 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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