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--- |
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license: apache-2.0 |
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base_model: microsoft/resnet-50 |
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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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model-index: |
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- name: resnet-Alzheimer |
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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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# resnet-Alzheimer |
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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on [Falah/Alzheimer_MRI](https://huggingface.co/datasets/Falah/Alzheimer_MRI). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0932 |
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- Accuracy: 0.9795 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.002 |
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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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.0127 | 1.0 | 80 | 0.9888 | 0.5088 | |
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| 0.9345 | 2.0 | 160 | 0.9422 | 0.5303 | |
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| 0.8889 | 3.0 | 240 | 0.8724 | 0.5781 | |
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| 0.8843 | 4.0 | 320 | 0.8536 | 0.5889 | |
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| 0.8397 | 5.0 | 400 | 0.8354 | 0.6152 | |
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| 0.8624 | 6.0 | 480 | 0.9221 | 0.5381 | |
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| 0.7543 | 7.0 | 560 | 0.7568 | 0.6475 | |
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| 0.6993 | 8.0 | 640 | 0.8830 | 0.6133 | |
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| 0.7045 | 9.0 | 720 | 0.7373 | 0.6582 | |
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| 0.6557 | 10.0 | 800 | 0.6076 | 0.7451 | |
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| 0.5876 | 11.0 | 880 | 0.7281 | 0.6992 | |
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| 0.5732 | 12.0 | 960 | 0.5769 | 0.7510 | |
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| 0.4864 | 13.0 | 1040 | 0.4457 | 0.8311 | |
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| 0.5175 | 14.0 | 1120 | 0.5278 | 0.7842 | |
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| 0.4865 | 15.0 | 1200 | 0.4164 | 0.8379 | |
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| 0.4049 | 16.0 | 1280 | 0.4204 | 0.8301 | |
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| 0.4167 | 17.0 | 1360 | 0.4720 | 0.8281 | |
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| 0.36 | 18.0 | 1440 | 0.4660 | 0.8164 | |
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| 0.3195 | 19.0 | 1520 | 0.3064 | 0.8770 | |
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| 0.3652 | 20.0 | 1600 | 0.2571 | 0.9121 | |
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| 0.2794 | 21.0 | 1680 | 0.2450 | 0.9150 | |
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| 0.2704 | 22.0 | 1760 | 0.2391 | 0.9033 | |
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| 0.2612 | 23.0 | 1840 | 0.2352 | 0.9277 | |
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| 0.2425 | 24.0 | 1920 | 0.4720 | 0.8281 | |
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| 0.2567 | 25.0 | 2000 | 0.2296 | 0.9131 | |
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| 0.2302 | 26.0 | 2080 | 0.3067 | 0.8945 | |
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| 0.2358 | 27.0 | 2160 | 0.1776 | 0.9375 | |
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| 0.2173 | 28.0 | 2240 | 0.1596 | 0.9492 | |
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| 0.1798 | 29.0 | 2320 | 0.1548 | 0.9414 | |
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| 0.197 | 30.0 | 2400 | 0.1740 | 0.9570 | |
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| 0.1654 | 31.0 | 2480 | 0.1217 | 0.9668 | |
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| 0.1896 | 32.0 | 2560 | 0.2552 | 0.9258 | |
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| 0.1705 | 33.0 | 2640 | 0.1031 | 0.9727 | |
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| 0.1689 | 34.0 | 2720 | 0.1011 | 0.9688 | |
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| 0.1439 | 35.0 | 2800 | 0.1175 | 0.9648 | |
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| 0.1606 | 36.0 | 2880 | 0.1805 | 0.9443 | |
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| 0.1281 | 37.0 | 2960 | 0.1254 | 0.9678 | |
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| 0.1518 | 38.0 | 3040 | 0.1184 | 0.9648 | |
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| 0.1531 | 39.0 | 3120 | 0.0992 | 0.9736 | |
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| 0.132 | 40.0 | 3200 | 0.0920 | 0.9775 | |
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| 0.134 | 41.0 | 3280 | 0.1391 | 0.9639 | |
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| 0.1413 | 42.0 | 3360 | 0.1122 | 0.9717 | |
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| 0.1097 | 43.0 | 3440 | 0.1171 | 0.9678 | |
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| 0.1167 | 44.0 | 3520 | 0.1054 | 0.9766 | |
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| 0.1388 | 45.0 | 3600 | 0.0932 | 0.9795 | |
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| 0.1221 | 46.0 | 3680 | 0.0946 | 0.9766 | |
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| 0.1099 | 47.0 | 3760 | 0.1116 | 0.9756 | |
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| 0.1041 | 48.0 | 3840 | 0.1126 | 0.9746 | |
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| 0.1025 | 49.0 | 3920 | 0.1114 | 0.9756 | |
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| 0.0887 | 50.0 | 4000 | 0.1056 | 0.9756 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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