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--- |
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license: apache-2.0 |
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base_model: microsoft/swinv2-small-patch4-window8-256 |
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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: swinv2-small-patch4-window8-256-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: 0.6875 |
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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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# swinv2-small-patch4-window8-256-finetuned-eurosat |
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This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window8-256](https://huggingface.co/microsoft/swinv2-small-patch4-window8-256) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2717 |
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- Accuracy: 0.6875 |
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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: 20 |
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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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| No log | 1.0 | 4 | 0.6579 | 0.6339 | |
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| No log | 2.0 | 8 | 0.7129 | 0.5 | |
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| 0.6364 | 3.0 | 12 | 0.6774 | 0.5982 | |
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| 0.6364 | 4.0 | 16 | 0.6584 | 0.6786 | |
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| 0.3486 | 5.0 | 20 | 0.6864 | 0.6786 | |
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| 0.3486 | 6.0 | 24 | 0.8473 | 0.6429 | |
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| 0.3486 | 7.0 | 28 | 0.9735 | 0.6339 | |
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| 0.1224 | 8.0 | 32 | 0.8121 | 0.6964 | |
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| 0.1224 | 9.0 | 36 | 1.2379 | 0.6429 | |
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| 0.0424 | 10.0 | 40 | 1.1585 | 0.6875 | |
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| 0.0424 | 11.0 | 44 | 1.5274 | 0.6161 | |
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| 0.0424 | 12.0 | 48 | 1.1415 | 0.6607 | |
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| 0.0353 | 13.0 | 52 | 1.4422 | 0.6518 | |
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| 0.0353 | 14.0 | 56 | 1.6677 | 0.625 | |
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| 0.0141 | 15.0 | 60 | 1.1960 | 0.6696 | |
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| 0.0141 | 16.0 | 64 | 1.5515 | 0.625 | |
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| 0.0141 | 17.0 | 68 | 1.7990 | 0.6161 | |
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| 0.0135 | 18.0 | 72 | 1.4437 | 0.6607 | |
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| 0.0135 | 19.0 | 76 | 1.2816 | 0.7054 | |
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| 0.0073 | 20.0 | 80 | 1.2717 | 0.6875 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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