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--- |
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license: apache-2.0 |
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library_name: peft |
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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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- precision |
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- recall |
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- f1 |
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base_model: microsoft/beit-base-patch16-224-pt22k-ft22k |
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model-index: |
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- name: chest-beit-base-finetuned |
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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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# chest-beit-base-finetuned |
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This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2620 |
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- Accuracy: 0.9107 |
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- Precision: 0.8923 |
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- Recall: 0.8923 |
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- F1: 0.8923 |
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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: 0.005 |
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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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- num_epochs: 10 |
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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 | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.4775 | 0.99 | 63 | 0.2264 | 0.9142 | 0.8850 | 0.8962 | 0.8903 | |
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| 0.7117 | 1.99 | 127 | 0.4008 | 0.7391 | 0.3695 | 0.5 | 0.4250 | |
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| 0.4115 | 3.0 | 191 | 0.4358 | 0.8155 | 0.7871 | 0.8645 | 0.7957 | |
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| 0.3631 | 4.0 | 255 | 0.3091 | 0.8798 | 0.8381 | 0.8708 | 0.8518 | |
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| 0.3794 | 4.99 | 318 | 0.2802 | 0.8798 | 0.8393 | 0.8623 | 0.8495 | |
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| 0.3713 | 5.99 | 382 | 0.2805 | 0.8773 | 0.8371 | 0.8542 | 0.8449 | |
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| 0.3953 | 7.0 | 446 | 0.3397 | 0.8584 | 0.8185 | 0.8872 | 0.8367 | |
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| 0.3218 | 8.0 | 510 | 0.3072 | 0.8670 | 0.8257 | 0.8898 | 0.8448 | |
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| 0.3219 | 8.99 | 573 | 0.2633 | 0.8961 | 0.8582 | 0.8872 | 0.8708 | |
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| 0.3049 | 9.88 | 630 | 0.2739 | 0.8927 | 0.8528 | 0.8912 | 0.8685 | |
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### Framework versions |
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- PEFT 0.9.0 |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
