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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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- precision |
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- recall |
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model-index: |
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- name: swin-tiny-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: validation |
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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: 0.8033333333333333 |
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- name: Precision |
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type: precision |
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value: 0.7970708748615725 |
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- name: Recall |
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type: recall |
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value: 0.8033333333333333 |
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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 |
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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: 0.4788 |
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- Accuracy: 0.8033 |
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- Precision: 0.7971 |
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- Recall: 0.8033 |
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- F1 Score: 0.7802 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
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| No log | 1.0 | 4 | 0.5946 | 0.7333 | 0.5378 | 0.7333 | 0.6205 | |
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| No log | 2.0 | 8 | 0.6006 | 0.7333 | 0.5378 | 0.7333 | 0.6205 | |
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| No log | 3.0 | 12 | 0.5677 | 0.7333 | 0.5378 | 0.7333 | 0.6205 | |
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| No log | 4.0 | 16 | 0.5616 | 0.7333 | 0.5378 | 0.7333 | 0.6205 | |
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| No log | 5.0 | 20 | 0.5556 | 0.75 | 0.7193 | 0.75 | 0.7023 | |
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| No log | 6.0 | 24 | 0.5435 | 0.7667 | 0.7819 | 0.7667 | 0.7019 | |
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| No log | 7.0 | 28 | 0.5318 | 0.7792 | 0.7885 | 0.7792 | 0.7281 | |
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| 0.5745 | 8.0 | 32 | 0.5316 | 0.7542 | 0.7262 | 0.7542 | 0.7126 | |
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| 0.5745 | 9.0 | 36 | 0.5232 | 0.7667 | 0.7533 | 0.7667 | 0.7185 | |
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| 0.5745 | 10.0 | 40 | 0.5226 | 0.7708 | 0.7639 | 0.7708 | 0.7217 | |
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| 0.5745 | 11.0 | 44 | 0.5217 | 0.7708 | 0.7597 | 0.7708 | 0.7253 | |
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| 0.5745 | 12.0 | 48 | 0.5224 | 0.7625 | 0.7561 | 0.7625 | 0.7034 | |
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| 0.5745 | 13.0 | 52 | 0.5213 | 0.7708 | 0.7510 | 0.7708 | 0.7409 | |
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| 0.5745 | 14.0 | 56 | 0.5207 | 0.7667 | 0.7709 | 0.7667 | 0.7064 | |
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| 0.4741 | 15.0 | 60 | 0.5247 | 0.7583 | 0.7343 | 0.7583 | 0.7334 | |
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| 0.4741 | 16.0 | 64 | 0.5352 | 0.7708 | 0.7639 | 0.7708 | 0.7217 | |
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| 0.4741 | 17.0 | 68 | 0.5227 | 0.7708 | 0.7507 | 0.7708 | 0.7460 | |
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| 0.4741 | 18.0 | 72 | 0.5206 | 0.7583 | 0.7564 | 0.7583 | 0.6912 | |
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| 0.4741 | 19.0 | 76 | 0.5088 | 0.775 | 0.7627 | 0.775 | 0.7353 | |
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| 0.4741 | 20.0 | 80 | 0.5144 | 0.7667 | 0.7503 | 0.7667 | 0.7221 | |
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| 0.4741 | 21.0 | 84 | 0.5227 | 0.7875 | 0.7918 | 0.7875 | 0.7453 | |
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| 0.4741 | 22.0 | 88 | 0.5150 | 0.775 | 0.7564 | 0.775 | 0.7494 | |
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| 0.4233 | 23.0 | 92 | 0.5240 | 0.7667 | 0.7533 | 0.7667 | 0.7185 | |
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| 0.4233 | 24.0 | 96 | 0.5156 | 0.7792 | 0.7684 | 0.7792 | 0.7418 | |
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| 0.4233 | 25.0 | 100 | 0.5141 | 0.7792 | 0.7631 | 0.7792 | 0.7503 | |
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| 0.4233 | 26.0 | 104 | 0.5234 | 0.7833 | 0.7813 | 0.7833 | 0.7420 | |
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| 0.4233 | 27.0 | 108 | 0.5175 | 0.7833 | 0.7813 | 0.7833 | 0.7420 | |
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| 0.4233 | 28.0 | 112 | 0.5122 | 0.7958 | 0.7856 | 0.7958 | 0.7715 | |
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| 0.4233 | 29.0 | 116 | 0.5126 | 0.7958 | 0.7856 | 0.7958 | 0.7715 | |
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| 0.3931 | 30.0 | 120 | 0.5130 | 0.7958 | 0.7856 | 0.7958 | 0.7715 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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