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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-small-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: deit-small-patch16-224-finetuned-eurosat
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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: 0.7977542108546475
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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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# deit-small-patch16-224-finetuned-eurosat
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This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6817
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- Accuracy: 0.7978
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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: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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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: 15
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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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| 1.5174 | 0.9966 | 218 | 1.3672 | 0.5855 |
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| 1.282 | 1.9977 | 437 | 1.1843 | 0.6260 |
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| 1.117 | 2.9989 | 656 | 1.0301 | 0.6845 |
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| 1.0176 | 4.0 | 875 | 0.9670 | 0.7070 |
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| 0.9912 | 4.9966 | 1093 | 0.8551 | 0.7477 |
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| 0.9458 | 5.9977 | 1312 | 0.8534 | 0.7392 |
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| 0.8502 | 6.9989 | 1531 | 0.8049 | 0.7600 |
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| 0.8954 | 8.0 | 1750 | 0.7716 | 0.7683 |
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| 0.872 | 8.9966 | 1968 | 0.7443 | 0.7779 |
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| 0.8186 | 9.9977 | 2187 | 0.7304 | 0.7835 |
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| 0.747 | 10.9989 | 2406 | 0.7178 | 0.7911 |
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| 0.6843 | 12.0 | 2625 | 0.7062 | 0.7925 |
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| 0.7453 | 12.9966 | 2843 | 0.7031 | 0.7939 |
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| 0.7472 | 13.9977 | 3062 | 0.6891 | 0.7965 |
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| 0.7067 | 14.9486 | 3270 | 0.6817 | 0.7978 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.1+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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