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tifosiai
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vri-alzheirmer-classifier

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Inference Endpoints
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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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+ ---
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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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+
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+ # alz-mri-vit
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+
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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 an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2447
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+ - F1: 0.9086
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 1.1101 | 1.0 | 64 | 0.9590 | 0.5549 |
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+ | 0.9356 | 2.0 | 128 | 0.8877 | 0.5940 |
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+ | 0.9187 | 3.0 | 192 | 0.9376 | 0.5272 |
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+ | 0.8804 | 4.0 | 256 | 0.8667 | 0.5962 |
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+ | 0.7854 | 5.0 | 320 | 0.7756 | 0.6720 |
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+ | 0.7278 | 6.0 | 384 | 0.7202 | 0.6860 |
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+ | 0.6462 | 7.0 | 448 | 0.7124 | 0.6898 |
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+ | 0.5731 | 8.0 | 512 | 0.6027 | 0.7553 |
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+ | 0.473 | 9.0 | 576 | 0.5520 | 0.7724 |
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+ | 0.4378 | 10.0 | 640 | 0.5550 | 0.7758 |
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+ | 0.4086 | 11.0 | 704 | 0.4366 | 0.8271 |
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+ | 0.36 | 12.0 | 768 | 0.4446 | 0.8225 |
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+ | 0.3217 | 13.0 | 832 | 0.3841 | 0.8441 |
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+ | 0.2941 | 14.0 | 896 | 0.4719 | 0.8182 |
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+ | 0.2679 | 15.0 | 960 | 0.4112 | 0.8410 |
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+ | 0.2565 | 16.0 | 1024 | 0.3698 | 0.8527 |
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+ | 0.2502 | 17.0 | 1088 | 0.3283 | 0.8810 |
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+ | 0.2166 | 18.0 | 1152 | 0.3569 | 0.8627 |
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+ | 0.197 | 19.0 | 1216 | 0.3475 | 0.8699 |
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+ | 0.2004 | 20.0 | 1280 | 0.3171 | 0.8834 |
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+ | 0.1722 | 21.0 | 1344 | 0.2711 | 0.8998 |
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+ | 0.1529 | 22.0 | 1408 | 0.2432 | 0.9100 |
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+ | 0.1495 | 23.0 | 1472 | 0.2950 | 0.8978 |
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+ | 0.1307 | 24.0 | 1536 | 0.2811 | 0.9034 |
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+ | 0.1278 | 25.0 | 1600 | 0.2545 | 0.9086 |
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+ | 0.1175 | 26.0 | 1664 | 0.2561 | 0.9051 |
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+ | 0.1264 | 27.0 | 1728 | 0.2128 | 0.9186 |
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+ | 0.1015 | 28.0 | 1792 | 0.3022 | 0.9014 |
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+ | 0.1077 | 29.0 | 1856 | 0.2403 | 0.9221 |
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+ | 0.0932 | 30.0 | 1920 | 0.2447 | 0.9086 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 2.3.1+cu121
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+ - Datasets 2.19.2
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+ - Tokenizers 0.19.1