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--- |
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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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- imagefolder |
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metrics: |
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- accuracy |
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- recall |
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- f1 |
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- precision |
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model-index: |
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- name: swin-base-patch4-window7-224-finetuned-ind-17-imbalanced-aadhaarmask |
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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: 0.855683269476373 |
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- name: Recall |
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type: recall |
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value: 0.855683269476373 |
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- name: F1 |
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type: f1 |
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value: 0.8542203503644927 |
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- name: Precision |
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type: precision |
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value: 0.8559779206156822 |
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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-finetuned-ind-17-imbalanced-aadhaarmask |
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This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-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: 0.3209 |
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- Accuracy: 0.8557 |
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- Recall: 0.8557 |
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- F1: 0.8542 |
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- Precision: 0.8560 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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 | Recall | F1 | Precision | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 0.5155 | 0.9974 | 293 | 0.5710 | 0.7935 | 0.7935 | 0.7821 | 0.7895 | |
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| 0.4245 | 1.9983 | 587 | 0.4729 | 0.8238 | 0.8238 | 0.8187 | 0.8266 | |
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| 0.4183 | 2.9991 | 881 | 0.4145 | 0.8408 | 0.8408 | 0.8309 | 0.8350 | |
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| 0.4088 | 4.0 | 1175 | 0.3901 | 0.8425 | 0.8425 | 0.8375 | 0.8501 | |
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| 0.3489 | 4.9974 | 1468 | 0.3703 | 0.8463 | 0.8463 | 0.8446 | 0.8518 | |
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| 0.3115 | 5.9983 | 1762 | 0.3500 | 0.8540 | 0.8540 | 0.8525 | 0.8605 | |
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| 0.3087 | 6.9991 | 2056 | 0.3338 | 0.8519 | 0.8519 | 0.8494 | 0.8582 | |
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| 0.2372 | 8.0 | 2350 | 0.3181 | 0.8548 | 0.8548 | 0.8543 | 0.8587 | |
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| 0.2816 | 8.9974 | 2643 | 0.3167 | 0.8536 | 0.8536 | 0.8530 | 0.8561 | |
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| 0.2378 | 9.9745 | 2930 | 0.3063 | 0.8702 | 0.8702 | 0.8686 | 0.8709 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.0a0+81ea7a4 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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