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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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model-index: |
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- name: alz-mri-vit |
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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: Falah/Alzheimer_MRI |
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type: Falah/Alzheimer_MRI |
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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: f1 |
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type: f1 |
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value: 0.930865 |
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datasets: |
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- Falah/Alzheimer_MRI |
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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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# alz-mri-vit |
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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 Falah/Alzheimer_MRI dataset (fine-tuning procedure is described [here](https://huggingface.co/spolivin/alz-mri-vit/blob/main/vit_finetuning.ipynb)). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1875 |
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- F1: 0.9309 |
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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: 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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 30 |
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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 | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 1.1218 | 1.0 | 64 | 0.9419 | 0.5742 | |
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| 0.94 | 2.0 | 128 | 0.9054 | 0.6029 | |
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| 0.9123 | 3.0 | 192 | 0.9019 | 0.5262 | |
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| 0.8625 | 4.0 | 256 | 0.8465 | 0.6029 | |
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| 0.8104 | 5.0 | 320 | 0.7810 | 0.6319 | |
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| 0.7244 | 6.0 | 384 | 0.7278 | 0.7037 | |
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| 0.697 | 7.0 | 448 | 0.6300 | 0.7480 | |
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| 0.5865 | 8.0 | 512 | 0.5659 | 0.7662 | |
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| 0.5199 | 9.0 | 576 | 0.5445 | 0.7721 | |
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| 0.4734 | 10.0 | 640 | 0.6750 | 0.7185 | |
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| 0.4399 | 11.0 | 704 | 0.4893 | 0.8274 | |
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| 0.3817 | 12.0 | 768 | 0.5578 | 0.7844 | |
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| 0.3318 | 13.0 | 832 | 0.4699 | 0.8228 | |
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| 0.3096 | 14.0 | 896 | 0.4460 | 0.8399 | |
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| 0.2787 | 15.0 | 960 | 0.4105 | 0.8399 | |
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| 0.2517 | 16.0 | 1024 | 0.3488 | 0.8578 | |
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| 0.2346 | 17.0 | 1088 | 0.3877 | 0.8773 | |
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| 0.2286 | 18.0 | 1152 | 0.3420 | 0.8575 | |
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| 0.1914 | 19.0 | 1216 | 0.4123 | 0.8682 | |
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| 0.1844 | 20.0 | 1280 | 0.2894 | 0.8913 | |
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| 0.173 | 21.0 | 1344 | 0.3197 | 0.8887 | |
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| 0.1687 | 22.0 | 1408 | 0.2626 | 0.9075 | |
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| 0.1601 | 23.0 | 1472 | 0.2951 | 0.9068 | |
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| 0.1466 | 24.0 | 1536 | 0.2666 | 0.9049 | |
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| 0.1468 | 25.0 | 1600 | 0.2136 | 0.9103 | |
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| 0.1226 | 26.0 | 1664 | 0.2387 | 0.9127 | |
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| 0.1186 | 27.0 | 1728 | 0.2131 | 0.9271 | |
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| 0.0951 | 28.0 | 1792 | 0.2520 | 0.9130 | |
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| 0.1049 | 29.0 | 1856 | 0.2096 | 0.9259 | |
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| 0.0936 | 30.0 | 1920 | 0.1875 | 0.9309 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
