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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: beit-large-patch16-224-finetuned-eurosat-50
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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: Augmented-Final
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split: train
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args: Augmented-Final
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9856115107913669
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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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# beit-large-patch16-224-finetuned-eurosat-50
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This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0568
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- Accuracy: 0.9856
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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-06
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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: 2
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- total_train_batch_size: 32
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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: 0.9
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- num_epochs: 12
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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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| 1.7148 | 1.0 | 122 | 1.6402 | 0.3916 |
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| 1.1543 | 2.0 | 244 | 1.0718 | 0.6208 |
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| 0.8948 | 3.0 | 366 | 0.7228 | 0.7564 |
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| 0.6348 | 4.0 | 488 | 0.5327 | 0.8160 |
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| 0.647 | 5.0 | 610 | 0.4081 | 0.8551 |
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| 0.3244 | 6.0 | 732 | 0.2965 | 0.9096 |
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| 0.305 | 7.0 | 854 | 0.2515 | 0.9342 |
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| 0.3522 | 8.0 | 976 | 0.1667 | 0.9568 |
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| 0.1782 | 9.0 | 1098 | 0.1494 | 0.9568 |
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| 0.1849 | 10.0 | 1220 | 0.0972 | 0.9712 |
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| 0.1814 | 11.0 | 1342 | 0.0559 | 0.9846 |
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| 0.1682 | 12.0 | 1464 | 0.0568 | 0.9856 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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