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--- |
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license: apache-2.0 |
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base_model: microsoft/swinv2-base-patch4-window12-192-22k |
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tags: |
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- generated_from_trainer |
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datasets: |
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- p1atdev/pvc |
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metrics: |
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- accuracy |
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model-index: |
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- name: pvc-quality-swinv2-base |
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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: test |
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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: 0.5317220543806647 |
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library_name: transformers |
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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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# pvc-quality-swinv2-base |
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This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-base-patch4-window12-192-22k) on the [pvc figure images dataset](https://huggingface.co/datasets/p1atdev/pvc). |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2396 |
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- Accuracy: 0.5317 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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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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| 1.7254 | 0.98 | 39 | 1.4826 | 0.4109 | |
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| 1.3316 | 1.99 | 79 | 1.2177 | 0.5136 | |
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| 1.0864 | 2.99 | 119 | 1.3006 | 0.4653 | |
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| 0.8572 | 4.0 | 159 | 1.2090 | 0.5015 | |
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| 0.7466 | 4.98 | 198 | 1.2150 | 0.5378 | |
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| 0.5986 | 5.99 | 238 | 1.4600 | 0.4955 | |
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| 0.4784 | 6.99 | 278 | 1.4131 | 0.5196 | |
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| 0.3525 | 8.0 | 318 | 1.5256 | 0.4985 | |
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| 0.3472 | 8.98 | 357 | 1.3883 | 0.5166 | |
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| 0.3281 | 9.81 | 390 | 1.5012 | 0.4955 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
