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--- |
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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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- f1 |
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model-index: |
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- name: dit-base-finetuned-brs |
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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: 0.8235294117647058 |
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- name: F1 |
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type: f1 |
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value: 0.8421052631578948 |
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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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# dit-base-finetuned-brs |
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This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2679 |
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- Accuracy: 0.8235 |
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- F1: 0.8421 |
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- Precision (ppv): 0.8889 |
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- Recall (sensitivity): 0.8 |
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- Specificity: 0.8571 |
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- Npv: 0.75 |
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- Auc: 0.8286 |
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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: 1e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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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: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision (ppv) | Recall (sensitivity) | Specificity | Npv | Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:| |
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| 0.6951 | 6.25 | 100 | 0.7100 | 0.4706 | 0.4 | 0.6 | 0.3 | 0.7143 | 0.4167 | 0.5071 | |
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| 0.5094 | 12.49 | 200 | 0.6511 | 0.5294 | 0.6 | 0.6 | 0.6 | 0.4286 | 0.4286 | 0.5143 | |
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| 0.5338 | 18.74 | 300 | 0.6113 | 0.6471 | 0.6667 | 0.75 | 0.6 | 0.7143 | 0.5556 | 0.6571 | |
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| 0.444 | 24.98 | 400 | 0.7057 | 0.6471 | 0.625 | 0.8333 | 0.5 | 0.8571 | 0.5455 | 0.6786 | |
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| 0.3877 | 31.25 | 500 | 0.7836 | 0.7059 | 0.7368 | 0.7778 | 0.7 | 0.7143 | 0.625 | 0.7071 | |
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| 0.6238 | 37.49 | 600 | 0.8340 | 0.7059 | 0.6667 | 1.0 | 0.5 | 1.0 | 0.5833 | 0.75 | |
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| 0.6856 | 43.74 | 700 | 1.0278 | 0.7647 | 0.8000 | 0.8 | 0.8 | 0.7143 | 0.7143 | 0.7571 | |
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| 0.487 | 49.98 | 800 | 1.0279 | 0.7647 | 0.7778 | 0.875 | 0.7 | 0.8571 | 0.6667 | 0.7786 | |
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| 0.4039 | 56.25 | 900 | 0.9028 | 0.7647 | 0.7778 | 0.875 | 0.7 | 0.8571 | 0.6667 | 0.7786 | |
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| 0.2214 | 62.49 | 1000 | 0.6894 | 0.8235 | 0.8235 | 1.0 | 0.7 | 1.0 | 0.7 | 0.85 | |
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| 0.7441 | 68.74 | 1100 | 1.1261 | 0.8235 | 0.8421 | 0.8889 | 0.8 | 0.8571 | 0.75 | 0.8286 | |
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| 0.5714 | 74.98 | 1200 | 0.8956 | 0.8235 | 0.8235 | 1.0 | 0.7 | 1.0 | 0.7 | 0.85 | |
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| 0.3093 | 81.25 | 1300 | 1.2498 | 0.7059 | 0.7059 | 0.8571 | 0.6 | 0.8571 | 0.6 | 0.7286 | |
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| 0.6528 | 87.49 | 1400 | 1.6744 | 0.7647 | 0.7778 | 0.875 | 0.7 | 0.8571 | 0.6667 | 0.7786 | |
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| 0.3314 | 93.74 | 1500 | 1.8034 | 0.7059 | 0.7059 | 0.8571 | 0.6 | 0.8571 | 0.6 | 0.7286 | |
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| 0.3617 | 99.98 | 1600 | 1.2679 | 0.8235 | 0.8421 | 0.8889 | 0.8 | 0.8571 | 0.75 | 0.8286 | |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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