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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: timm/levit_128.fb_dist_in1k |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: levit_128.fb_dist_in1k-finetuned-stroke-binary |
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results: [] |
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datasets: |
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- BTX24/tekno21-brain-stroke-dataset-binary |
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pipeline_tag: image-classification |
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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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# levit_128.fb_dist_in1k-finetuned-stroke-binary |
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This model is a fine-tuned version of [timm/levit_128.fb_dist_in1k](https://huggingface.co/timm/levit_128.fb_dist_in1k) on an binary stroke detection dataset. |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.8598 |
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- F1: 0.8577 |
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- Precision: 0.8602 |
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- Recall: 0.8598 |
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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: 2e-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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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 36 |
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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 | F1 | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.7002 | 0.6202 | 100 | nan | 0.5690 | 0.5387 | 0.5349 | 0.5690 | |
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| 0.681 | 1.2357 | 200 | nan | 0.5834 | 0.5331 | 0.5372 | 0.5834 | |
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| 0.6874 | 1.8558 | 300 | nan | 0.6002 | 0.5596 | 0.5665 | 0.6002 | |
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| 0.6774 | 2.4713 | 400 | nan | 0.6124 | 0.5811 | 0.5867 | 0.6124 | |
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| 0.6533 | 3.0868 | 500 | nan | 0.6852 | 0.6694 | 0.6767 | 0.6852 | |
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| 0.6368 | 3.7070 | 600 | nan | 0.7205 | 0.7153 | 0.7153 | 0.7205 | |
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| 0.6196 | 4.3225 | 700 | nan | 0.7603 | 0.7471 | 0.7650 | 0.7603 | |
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| 0.5663 | 4.9426 | 800 | nan | 0.7883 | 0.7843 | 0.7864 | 0.7883 | |
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| 0.5196 | 5.5581 | 900 | nan | 0.8078 | 0.7972 | 0.8206 | 0.8078 | |
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| 0.4704 | 6.1736 | 1000 | nan | 0.8363 | 0.8317 | 0.8396 | 0.8363 | |
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| 0.4715 | 6.7938 | 1100 | nan | 0.8349 | 0.8292 | 0.8409 | 0.8349 | |
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| 0.452 | 7.4093 | 1200 | nan | 0.8503 | 0.8479 | 0.8505 | 0.8503 | |
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| 0.4538 | 8.0248 | 1300 | nan | 0.8598 | 0.8577 | 0.8602 | 0.8598 | |
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### Framework versions |
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- Transformers 4.48.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.4.0 |
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- Tokenizers 0.21.0 |
