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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- image-classification |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-weldclassifyv2 |
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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: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8633093525179856 |
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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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# vit-weldclassifyv2 |
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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 imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4613 |
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- Accuracy: 0.8633 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 13 |
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- mixed_precision_training: Native AMP |
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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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| 1.035 | 0.6410 | 100 | 1.1332 | 0.4029 | |
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| 0.6893 | 1.2821 | 200 | 0.7341 | 0.6655 | |
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| 0.5618 | 1.9231 | 300 | 0.5596 | 0.7554 | |
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| 0.4344 | 2.5641 | 400 | 0.5951 | 0.7770 | |
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| 0.1591 | 3.2051 | 500 | 0.4667 | 0.8453 | |
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| 0.1821 | 3.8462 | 600 | 0.5082 | 0.8345 | |
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| 0.0811 | 4.4872 | 700 | 0.4613 | 0.8633 | |
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| 0.1729 | 5.1282 | 800 | 0.6382 | 0.7986 | |
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| 0.1174 | 5.7692 | 900 | 0.4974 | 0.8669 | |
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| 0.0389 | 6.4103 | 1000 | 0.6049 | 0.8453 | |
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| 0.0099 | 7.0513 | 1100 | 0.6147 | 0.8561 | |
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| 0.0342 | 7.6923 | 1200 | 0.5603 | 0.8741 | |
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| 0.0175 | 8.3333 | 1300 | 0.5679 | 0.8849 | |
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| 0.0177 | 8.9744 | 1400 | 0.6592 | 0.8669 | |
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| 0.0025 | 9.6154 | 1500 | 0.6000 | 0.8669 | |
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| 0.0021 | 10.2564 | 1600 | 0.6060 | 0.8597 | |
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| 0.002 | 10.8974 | 1700 | 0.6113 | 0.8597 | |
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| 0.0019 | 11.5385 | 1800 | 0.6178 | 0.8561 | |
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| 0.0019 | 12.1795 | 1900 | 0.6214 | 0.8561 | |
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| 0.002 | 12.8205 | 2000 | 0.6228 | 0.8561 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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