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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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base_model: microsoft/swin-large-patch4-window7-224-in22k |
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datasets: |
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- medmnist-v2 |
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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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model-index: |
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- name: breastmnist-swin-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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# breastmnist-swin-base-finetuned |
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This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-large-patch4-window7-224-in22k) on the medmnist-v2 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3595 |
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- Accuracy: 0.8526 |
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- Precision: 0.8162 |
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- Recall: 0.8014 |
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- F1: 0.8082 |
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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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| No log | 0.9143 | 8 | 0.5069 | 0.7436 | 0.8701 | 0.5238 | 0.4708 | |
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| 0.6976 | 1.9429 | 17 | 0.4591 | 0.8590 | 0.8190 | 0.8283 | 0.8234 | |
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| 0.5351 | 2.9714 | 26 | 0.3745 | 0.8846 | 0.8667 | 0.8308 | 0.8462 | |
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| 0.4998 | 4.0 | 35 | 0.3243 | 0.8974 | 0.8697 | 0.8697 | 0.8697 | |
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| 0.4569 | 4.9143 | 43 | 0.4070 | 0.8590 | 0.8306 | 0.7982 | 0.8120 | |
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| 0.4182 | 5.9429 | 52 | 0.3801 | 0.8718 | 0.8439 | 0.8221 | 0.8319 | |
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| 0.4432 | 6.9714 | 61 | 0.3071 | 0.8718 | 0.8371 | 0.8371 | 0.8371 | |
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| 0.3988 | 8.0 | 70 | 0.3205 | 0.8718 | 0.8332 | 0.8521 | 0.8417 | |
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| 0.3988 | 8.9143 | 78 | 0.3239 | 0.8846 | 0.8506 | 0.8609 | 0.8555 | |
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| 0.3993 | 9.1429 | 80 | 0.3214 | 0.8846 | 0.8506 | 0.8609 | 0.8555 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
