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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-weldclassifyv3 |
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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: 0.920863309352518 |
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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-weldclassifyv3 |
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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: 0.2671 |
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- Accuracy: 0.9209 |
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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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| 0.8398 | 0.6410 | 100 | 1.0312 | 0.5036 | |
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| 0.5613 | 1.2821 | 200 | 0.7068 | 0.6619 | |
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| 0.4296 | 1.9231 | 300 | 0.4008 | 0.8309 | |
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| 0.3475 | 2.5641 | 400 | 0.3345 | 0.8813 | |
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| 0.1183 | 3.2051 | 500 | 0.4293 | 0.8489 | |
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| 0.1531 | 3.8462 | 600 | 0.2748 | 0.9137 | |
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| 0.1174 | 4.4872 | 700 | 0.3649 | 0.8813 | |
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| 0.0498 | 5.1282 | 800 | 0.3279 | 0.8921 | |
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| 0.0817 | 5.7692 | 900 | 0.2763 | 0.9353 | |
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| 0.0075 | 6.4103 | 1000 | 0.2671 | 0.9209 | |
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| 0.0265 | 7.0513 | 1100 | 0.3185 | 0.9209 | |
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| 0.0457 | 7.6923 | 1200 | 0.3776 | 0.9101 | |
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| 0.0032 | 8.3333 | 1300 | 0.2835 | 0.9388 | |
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| 0.0027 | 8.9744 | 1400 | 0.5365 | 0.8885 | |
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| 0.0024 | 9.6154 | 1500 | 0.2817 | 0.9460 | |
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| 0.0021 | 10.2564 | 1600 | 0.2890 | 0.9460 | |
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| 0.002 | 10.8974 | 1700 | 0.2934 | 0.9460 | |
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| 0.0019 | 11.5385 | 1800 | 0.2976 | 0.9460 | |
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| 0.0018 | 12.1795 | 1900 | 0.2996 | 0.9460 | |
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| 0.0018 | 12.8205 | 2000 | 0.3006 | 0.9460 | |
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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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