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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: swinv2-large-patch4-window12-192-22k-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: Skin_Cancer |
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split: train |
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args: Skin_Cancer |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7220338983050848 |
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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-large-patch4-window12-192-22k-finetuned-eurosat-50 |
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This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-large-patch4-window12-192-22k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6967 |
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- Accuracy: 0.7220 |
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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: 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: 0.005 |
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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 | 0.97 | 9 | 1.6984 | 0.3729 | |
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| No log | 1.95 | 18 | 1.5150 | 0.4881 | |
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| 1.6944 | 2.92 | 27 | 1.3304 | 0.5390 | |
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| 1.6944 | 4.0 | 37 | 1.1761 | 0.6 | |
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| 1.3633 | 4.97 | 46 | 1.0588 | 0.6373 | |
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| 1.3633 | 5.95 | 55 | 0.9952 | 0.6475 | |
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| 1.1208 | 6.92 | 64 | 0.9326 | 0.6610 | |
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| 1.1208 | 8.0 | 74 | 0.8785 | 0.6712 | |
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| 0.9891 | 8.97 | 83 | 0.8478 | 0.6746 | |
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| 0.9891 | 9.95 | 92 | 0.8144 | 0.6847 | |
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| 0.9011 | 10.92 | 101 | 0.7774 | 0.7017 | |
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| 0.9011 | 12.0 | 111 | 0.7567 | 0.6983 | |
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| 0.8143 | 12.97 | 120 | 0.7525 | 0.6949 | |
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| 0.8143 | 13.95 | 129 | 0.7309 | 0.7051 | |
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| 0.8143 | 14.92 | 138 | 0.7141 | 0.7119 | |
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| 0.7926 | 16.0 | 148 | 0.7095 | 0.7186 | |
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| 0.7926 | 16.97 | 157 | 0.7057 | 0.7220 | |
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| 0.7439 | 17.95 | 166 | 0.6988 | 0.7220 | |
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| 0.7439 | 18.92 | 175 | 0.6967 | 0.7220 | |
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| 0.7533 | 19.46 | 180 | 0.6967 | 0.7220 | |
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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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