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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: microsoft/swin-tiny-patch4-window7-224 |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: swin-tiny-patch4-window7-224-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: 0.8553054662379421 |
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- name: Precision |
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type: precision |
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value: 0.8675973805921082 |
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- name: Recall |
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type: recall |
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value: 0.8553054662379421 |
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- name: F1 |
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type: f1 |
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value: 0.8581712564304036 |
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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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# swin-tiny-patch4-window7-224-finetuned-eurosat |
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This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2816 |
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- Accuracy: 0.8553 |
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- Precision: 0.8676 |
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- Recall: 0.8553 |
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- F1: 0.8582 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.5793 | 1.0 | 22 | 0.5874 | 0.6785 | 0.4603 | 0.6785 | 0.5485 | |
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| 0.3711 | 2.0 | 44 | 0.4135 | 0.7781 | 0.8169 | 0.7781 | 0.7395 | |
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| 0.2961 | 3.0 | 66 | 0.2816 | 0.8553 | 0.8676 | 0.8553 | 0.8582 | |
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| 0.2576 | 4.0 | 88 | 0.2899 | 0.7942 | 0.7884 | 0.7942 | 0.7857 | |
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| 0.261 | 5.0 | 110 | 0.2469 | 0.8103 | 0.8057 | 0.8103 | 0.8037 | |
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| 0.2559 | 6.0 | 132 | 0.2548 | 0.8360 | 0.8632 | 0.8360 | 0.8179 | |
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| 0.2249 | 7.0 | 154 | 0.2835 | 0.8135 | 0.8479 | 0.8135 | 0.7882 | |
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| 0.2242 | 8.0 | 176 | 0.2335 | 0.8296 | 0.8261 | 0.8296 | 0.8262 | |
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| 0.2215 | 9.0 | 198 | 0.2293 | 0.8521 | 0.8549 | 0.8521 | 0.8532 | |
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| 0.2269 | 10.0 | 220 | 0.2213 | 0.8424 | 0.8396 | 0.8424 | 0.8393 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 3.0.2 |
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- Tokenizers 0.19.1 |
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