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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-Diatome |
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results: [] |
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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-Diatome |
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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 Diatome dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2285 |
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- Accuracy: 0.9429 |
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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: 4 |
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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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| 2.2009 | 0.23 | 100 | 1.9938 | 0.6045 | |
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| 1.2823 | 0.47 | 200 | 1.2293 | 0.7327 | |
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| 0.7569 | 0.7 | 300 | 0.9534 | 0.7868 | |
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| 0.7428 | 0.93 | 400 | 0.7906 | 0.8078 | |
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| 0.4309 | 1.16 | 500 | 0.5759 | 0.8538 | |
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| 0.349 | 1.4 | 600 | 0.5070 | 0.8742 | |
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| 0.517 | 1.63 | 700 | 0.5048 | 0.8794 | |
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| 0.3667 | 1.86 | 800 | 0.5212 | 0.8596 | |
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| 0.169 | 2.09 | 900 | 0.4112 | 0.8888 | |
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| 0.1443 | 2.33 | 1000 | 0.3294 | 0.9109 | |
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| 0.1389 | 2.56 | 1100 | 0.3146 | 0.9190 | |
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| 0.142 | 2.79 | 1200 | 0.2994 | 0.9208 | |
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| 0.0921 | 3.02 | 1300 | 0.2620 | 0.9324 | |
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| 0.0768 | 3.26 | 1400 | 0.2516 | 0.9336 | |
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| 0.061 | 3.49 | 1500 | 0.2425 | 0.9388 | |
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| 0.0729 | 3.72 | 1600 | 0.2335 | 0.9418 | |
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| 0.0757 | 3.95 | 1700 | 0.2285 | 0.9429 | |
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### Framework versions |
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- Transformers 4.27.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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