alperenoguz
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End of training
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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/deit-base-patch16-224
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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: smids_deit_base_f1
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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: test
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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: 0.8747913188647746
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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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# smids_deit_base_f1
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This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4898
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- Accuracy: 0.8748
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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: 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: 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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| 0.3398 | 1.0 | 375 | 0.4288 | 0.8164 |
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| 0.2944 | 2.0 | 750 | 0.4228 | 0.8297 |
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| 0.1957 | 3.0 | 1125 | 0.4014 | 0.8497 |
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| 0.176 | 4.0 | 1501 | 0.4565 | 0.8514 |
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| 0.1333 | 5.0 | 1876 | 0.3698 | 0.8731 |
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| 0.1322 | 6.0 | 2251 | 0.5002 | 0.8481 |
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| 0.0952 | 7.0 | 2626 | 0.4711 | 0.8648 |
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| 0.0941 | 8.0 | 3002 | 0.4872 | 0.8698 |
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| 0.0946 | 9.0 | 3377 | 0.5003 | 0.8564 |
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| 0.0911 | 9.99 | 3750 | 0.4898 | 0.8748 |
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### Framework versions
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- Transformers 4.32.1
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- Pytorch 2.1.0+cu121
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- Datasets 2.12.0
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- Tokenizers 0.13.2
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