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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/vit-base-patch32-384 |
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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-base-patch32-384-finetuned-eurosat-albumentations |
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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: 0.9726027397260274 |
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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-base-patch32-384-finetuned-eurosat-albumentations |
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This model is a fine-tuned version of [google/vit-base-patch32-384](https://huggingface.co./google/vit-base-patch32-384) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1871 |
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- Accuracy: 0.9726 |
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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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 50 |
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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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| 0.7204 | 0.9412 | 12 | 0.5695 | 0.7397 | |
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| 0.4269 | 1.9804 | 25 | 0.2537 | 0.9178 | |
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| 0.1605 | 2.9412 | 37 | 0.3347 | 0.8767 | |
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| 0.0758 | 3.9804 | 50 | 0.2203 | 0.9041 | |
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| 0.0405 | 4.9412 | 62 | 0.3563 | 0.9178 | |
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| 0.0358 | 5.9804 | 75 | 0.2326 | 0.9315 | |
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| 0.0188 | 6.9412 | 87 | 0.2046 | 0.9315 | |
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| 0.026 | 7.9804 | 100 | 0.2195 | 0.8904 | |
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| 0.0582 | 8.9412 | 112 | 0.3378 | 0.9178 | |
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| 0.0113 | 9.9804 | 125 | 0.2685 | 0.9178 | |
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| 0.0081 | 10.9412 | 137 | 0.2443 | 0.9315 | |
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| 0.0091 | 11.9804 | 150 | 0.4675 | 0.9041 | |
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| 0.0065 | 12.9412 | 162 | 0.3252 | 0.9452 | |
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| 0.0026 | 13.9804 | 175 | 0.1871 | 0.9726 | |
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| 0.0043 | 14.9412 | 187 | 0.2256 | 0.9589 | |
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| 0.0094 | 15.9804 | 200 | 0.1980 | 0.9452 | |
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| 0.0028 | 16.9412 | 212 | 0.2928 | 0.9315 | |
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| 0.0003 | 17.9804 | 225 | 0.2241 | 0.9726 | |
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| 0.0006 | 18.9412 | 237 | 0.2396 | 0.9726 | |
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| 0.0012 | 19.9804 | 250 | 0.2663 | 0.9315 | |
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| 0.0001 | 20.9412 | 262 | 0.2266 | 0.9726 | |
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| 0.0002 | 21.9804 | 275 | 0.2637 | 0.9452 | |
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| 0.0001 | 22.9412 | 287 | 0.2873 | 0.9452 | |
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| 0.0003 | 23.9804 | 300 | 0.2068 | 0.9589 | |
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| 0.0001 | 24.9412 | 312 | 0.2485 | 0.9452 | |
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| 0.0047 | 25.9804 | 325 | 0.3375 | 0.9178 | |
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| 0.0015 | 26.9412 | 337 | 0.3132 | 0.9589 | |
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| 0.0001 | 27.9804 | 350 | 0.3148 | 0.9452 | |
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| 0.0025 | 28.9412 | 362 | 0.2533 | 0.9452 | |
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| 0.0038 | 29.9804 | 375 | 0.2860 | 0.9315 | |
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| 0.0025 | 30.9412 | 387 | 0.2785 | 0.9452 | |
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| 0.0031 | 31.9804 | 400 | 0.3246 | 0.9452 | |
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| 0.0 | 32.9412 | 412 | 0.3367 | 0.9452 | |
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| 0.0006 | 33.9804 | 425 | 0.2625 | 0.9726 | |
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| 0.0 | 34.9412 | 437 | 0.2689 | 0.9589 | |
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| 0.0007 | 35.9804 | 450 | 0.2891 | 0.9726 | |
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| 0.0003 | 36.9412 | 462 | 0.4523 | 0.9315 | |
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| 0.0003 | 37.9804 | 475 | 0.3426 | 0.9452 | |
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| 0.0001 | 38.9412 | 487 | 0.3167 | 0.9589 | |
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| 0.0 | 39.9804 | 500 | 0.3237 | 0.9589 | |
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| 0.0002 | 40.9412 | 512 | 0.3085 | 0.9589 | |
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| 0.0 | 41.9804 | 525 | 0.3095 | 0.9589 | |
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| 0.0 | 42.9412 | 537 | 0.3049 | 0.9589 | |
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| 0.0002 | 43.9804 | 550 | 0.3039 | 0.9589 | |
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| 0.0001 | 44.9412 | 562 | 0.3044 | 0.9589 | |
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| 0.0001 | 45.9804 | 575 | 0.3031 | 0.9726 | |
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| 0.0 | 46.9412 | 587 | 0.3028 | 0.9726 | |
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| 0.0 | 47.9804 | 600 | 0.3027 | 0.9726 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.20.3 |
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