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--- |
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license: apache-2.0 |
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base_model: microsoft/swinv2-base-patch4-window8-256 |
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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: swinv2-base-patch4-window8-256-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: 0.8463175819497658 |
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- name: Recall |
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type: recall |
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value: 0.8463175819497658 |
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- name: F1 |
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type: f1 |
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value: 0.8463640211224454 |
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- name: Precision |
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type: precision |
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value: 0.8481964005333177 |
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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-base-patch4-window8-256-finetuned-ind-17-imbalanced-aadhaarmask |
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This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3463 |
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- Accuracy: 0.8463 |
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- Recall: 0.8463 |
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- F1: 0.8464 |
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- Precision: 0.8482 |
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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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| No log | 0.9974 | 293 | 0.6222 | 0.7901 | 0.7901 | 0.7737 | 0.7747 | |
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| No log | 1.9983 | 587 | 0.4901 | 0.8063 | 0.8063 | 0.7998 | 0.8066 | |
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| No log | 2.9991 | 881 | 0.4374 | 0.8225 | 0.8225 | 0.8170 | 0.8356 | |
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| No log | 4.0 | 1175 | 0.4262 | 0.8340 | 0.8340 | 0.8270 | 0.8541 | |
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| No log | 4.9974 | 1468 | 0.4079 | 0.8310 | 0.8310 | 0.8290 | 0.8379 | |
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| No log | 5.9983 | 1762 | 0.4117 | 0.8370 | 0.8370 | 0.8361 | 0.8509 | |
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| No log | 6.9991 | 2056 | 0.3807 | 0.8370 | 0.8370 | 0.8361 | 0.8416 | |
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| No log | 8.0 | 2350 | 0.3419 | 0.8595 | 0.8595 | 0.8583 | 0.8609 | |
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| No log | 8.9974 | 2643 | 0.3628 | 0.8438 | 0.8438 | 0.8424 | 0.8448 | |
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| 0.4492 | 9.9745 | 2930 | 0.3638 | 0.8399 | 0.8399 | 0.8394 | 0.8410 | |
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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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