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--- |
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license: apache-2.0 |
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base_model: microsoft/resnet-18 |
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tags: |
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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: font-identifier |
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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: 0.9040816326530612 |
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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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# font-identifier |
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This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3626 |
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- Accuracy: 0.9041 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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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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| 3.929 | 0.98 | 30 | 3.8215 | 0.0429 | |
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| 3.2162 | 1.98 | 61 | 2.9144 | 0.2816 | |
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| 2.4387 | 2.99 | 92 | 2.1019 | 0.4776 | |
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| 1.9404 | 4.0 | 123 | 1.5607 | 0.6041 | |
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| 1.5756 | 4.98 | 153 | 1.3012 | 0.6449 | |
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| 1.3374 | 5.98 | 184 | 1.0699 | 0.7102 | |
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| 1.1912 | 6.99 | 215 | 0.9145 | 0.7633 | |
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| 1.0716 | 8.0 | 246 | 0.7864 | 0.7898 | |
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| 0.9751 | 8.98 | 276 | 0.6894 | 0.8204 | |
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| 0.8211 | 9.98 | 307 | 0.6256 | 0.8510 | |
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| 0.8254 | 10.99 | 338 | 0.5563 | 0.8633 | |
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| 0.742 | 12.0 | 369 | 0.5149 | 0.8694 | |
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| 0.6949 | 12.98 | 399 | 0.4625 | 0.8878 | |
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| 0.6401 | 13.98 | 430 | 0.4799 | 0.8857 | |
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| 0.6304 | 14.99 | 461 | 0.3970 | 0.8980 | |
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| 0.6239 | 16.0 | 492 | 0.4016 | 0.9 | |
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| 0.5911 | 16.98 | 522 | 0.4271 | 0.8755 | |
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| 0.5764 | 17.98 | 553 | 0.3922 | 0.9 | |
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| 0.5461 | 18.99 | 584 | 0.3750 | 0.9 | |
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| 0.6236 | 19.51 | 600 | 0.3626 | 0.9041 | |
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### Framework versions |
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- Transformers 4.36.0.dev0 |
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- Pytorch 2.0.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.14.1 |
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