update model card README.md
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README.md
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.
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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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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate:
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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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- 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:
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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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### Framework versions
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8328267477203647
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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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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4990
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- Accuracy: 0.8328
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## Model description
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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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- 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: 30
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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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| 1.3594 | 0.99 | 92 | 1.3630 | 0.5015 |
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| 1.3214 | 2.0 | 185 | 1.3252 | 0.5714 |
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| 1.2633 | 2.99 | 277 | 1.2851 | 0.6140 |
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| 1.2693 | 4.0 | 370 | 1.2385 | 0.6626 |
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| 1.1902 | 4.99 | 462 | 1.1837 | 0.6991 |
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| 1.1421 | 6.0 | 555 | 1.1255 | 0.7568 |
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| 1.1979 | 6.99 | 647 | 1.0094 | 0.8024 |
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| 0.9431 | 8.0 | 740 | 0.9544 | 0.8237 |
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| 0.9627 | 8.99 | 832 | 0.8864 | 0.8267 |
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| 0.8556 | 10.0 | 925 | 0.8365 | 0.8328 |
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| 0.7792 | 10.99 | 1017 | 0.7762 | 0.8359 |
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| 0.7941 | 12.0 | 1110 | 0.7467 | 0.8359 |
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| 0.8361 | 12.99 | 1202 | 0.7345 | 0.8237 |
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| 0.7757 | 14.0 | 1295 | 0.7228 | 0.8146 |
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| 0.6977 | 14.99 | 1387 | 0.6923 | 0.8267 |
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| 0.6874 | 16.0 | 1480 | 0.6540 | 0.8146 |
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| 0.6887 | 16.99 | 1572 | 0.6276 | 0.8298 |
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| 0.7204 | 18.0 | 1665 | 0.5989 | 0.8267 |
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| 0.8334 | 18.99 | 1757 | 0.6027 | 0.8237 |
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| 0.7654 | 20.0 | 1850 | 0.5699 | 0.8511 |
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| 0.7628 | 20.99 | 1942 | 0.5465 | 0.8389 |
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| 0.7874 | 22.0 | 2035 | 0.5621 | 0.8298 |
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| 0.8149 | 22.99 | 2127 | 0.5474 | 0.8298 |
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| 0.7565 | 24.0 | 2220 | 0.5388 | 0.8480 |
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| 0.7241 | 24.99 | 2312 | 0.5351 | 0.8267 |
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| 0.7894 | 26.0 | 2405 | 0.5327 | 0.8389 |
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| 0.7664 | 26.99 | 2497 | 0.5065 | 0.8450 |
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| 0.6655 | 28.0 | 2590 | 0.5309 | 0.8359 |
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| 0.607 | 28.99 | 2682 | 0.5061 | 0.8541 |
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| 0.6462 | 29.84 | 2760 | 0.4990 | 0.8328 |
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### Framework versions
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