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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-base-patch4-window7-224 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- kedimestan/RetinoblastomaHealthyImageDataset |
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metrics: |
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- accuracy |
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model-index: |
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- name: swin-base-patch4-window7-224 |
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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: 1 |
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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-base-patch4-window7-224 |
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Bu model, [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) modelin retinoblastoma veri seti için ince ayar yapılmış versiyonudur. |
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Test veri setindeki sonuçları aşağıdaki gibidir: |
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- Loss: 0.3809 |
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- Accuracy: 1.0 |
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## Model description |
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Swin tabanlı bir modeldir. 5e-5 öğrenme oranı ile 10 epoch ile eğitim yapıldı. Optimizasyon için Adam kullanıldı. veri seti %80-%20 olacak şekilde sırasyıla eğitim ve test verisetine bölündü. |
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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 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| No log | 0.5714 | 1 | 0.8119 | 0.3913 | |
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| No log | 1.7143 | 3 | 0.6544 | 0.5217 | |
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| No log | 2.8571 | 5 | 0.3809 | 1.0 | |
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| No log | 4.0 | 7 | 0.2352 | 1.0 | |
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| No log | 4.5714 | 8 | 0.1875 | 1.0 | |
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| 0.4988 | 5.7143 | 10 | 0.1481 | 1.0 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.19.1 |
