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update model card README.md
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README.md
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---
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license: apache-2.0
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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: dresses
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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: 0.9013840830449827
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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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# dresses
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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 the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4588
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- Accuracy: 0.9014
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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: 64
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- eval_batch_size: 8
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- seed: 42
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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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- num_epochs: 15
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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 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.2458 | 1.23 | 100 | 0.4519 | 0.8633 |
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| 0.0937 | 2.47 | 200 | 0.4285 | 0.8754 |
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| 0.0802 | 3.7 | 300 | 0.4683 | 0.8754 |
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| 0.041 | 4.94 | 400 | 0.4088 | 0.9031 |
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| 0.0277 | 6.17 | 500 | 0.3979 | 0.8945 |
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| 0.0459 | 7.41 | 600 | 0.4253 | 0.9014 |
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| 0.024 | 8.64 | 700 | 0.4680 | 0.8893 |
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| 0.0267 | 9.88 | 800 | 0.4575 | 0.8945 |
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| 0.019 | 11.11 | 900 | 0.4470 | 0.8893 |
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| 0.0235 | 12.35 | 1000 | 0.4380 | 0.9066 |
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| 0.0129 | 13.58 | 1100 | 0.4557 | 0.9048 |
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| 0.0211 | 14.81 | 1200 | 0.4588 | 0.9014 |
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### Framework versions
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- Transformers 4.23.1
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- Pytorch 1.12.1+cu113
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- Datasets 2.6.1
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- Tokenizers 0.13.1
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