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README.md
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---
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license: apache-2.0
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base_model: facebook/vit-msn-small
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: vit-msn-small-finetuned-alzheimers
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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: imagefolder
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type: imagefolder
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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: Accuracy
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type: accuracy
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value: 0.8625
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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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# vit-msn-small-finetuned-alzheimers
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This model is a fine-tuned version of [facebook/vit-msn-small](https://huggingface.co/facebook/vit-msn-small) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3612
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- Accuracy: 0.8625
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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: 5e-05
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- train_batch_size: 64
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- eval_batch_size: 64
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 256
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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: 10
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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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| 0.9297 | 0.9778 | 22 | 0.8769 | 0.6156 |
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| 0.8601 | 2.0 | 45 | 0.7799 | 0.6344 |
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| 0.7954 | 2.9778 | 67 | 0.7197 | 0.6828 |
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| 0.7468 | 4.0 | 90 | 0.7003 | 0.6734 |
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| 0.6935 | 4.9778 | 112 | 0.6064 | 0.7547 |
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| 0.6271 | 6.0 | 135 | 0.5648 | 0.7688 |
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| 0.5622 | 6.9778 | 157 | 0.4824 | 0.8094 |
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| 0.4815 | 8.0 | 180 | 0.4012 | 0.8609 |
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| 0.4771 | 8.9778 | 202 | 0.3799 | 0.8562 |
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| 0.4171 | 9.7778 | 220 | 0.3612 | 0.8625 |
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### Framework versions
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- Transformers 4.40.0
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- Pytorch 2.2.1+cu121
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- Datasets 2.19.0
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- Tokenizers 0.19.1
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