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--- |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: segformer-class-classWeights-augmentation |
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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: 0.9655172413793104 |
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- name: F1 |
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type: f1 |
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value: 0.964683592269799 |
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- name: Precision |
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type: precision |
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value: 0.9674329501915708 |
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- name: Recall |
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type: recall |
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value: 0.9655172413793104 |
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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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# segformer-class-classWeights-augmentation |
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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: 0.1855 |
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- Accuracy: 0.9655 |
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- F1: 0.9647 |
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- Precision: 0.9674 |
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- Recall: 0.9655 |
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- Learning Rate: 0.0000 |
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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: 10 |
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- eval_batch_size: 10 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 40 |
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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 | F1 | Precision | Recall | Rate | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| |
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| No log | 0.89 | 6 | 0.1113 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 | |
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| 0.1153 | 1.93 | 13 | 0.0929 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 | |
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| 0.2246 | 2.96 | 20 | 0.1026 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 | |
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| 0.2246 | 4.0 | 27 | 0.0391 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 | |
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| 0.1433 | 4.89 | 33 | 0.0673 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 | |
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| 0.1816 | 5.93 | 40 | 0.0794 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 | |
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| 0.1816 | 6.96 | 47 | 0.0687 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 | |
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| 0.1448 | 8.0 | 54 | 0.1123 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 | |
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| 0.1124 | 8.89 | 60 | 0.1855 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 | |
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### Framework versions |
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- Transformers 4.31.0 |
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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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