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--- |
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license: apache-2.0 |
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base_model: microsoft/swinv2-tiny-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-tiny-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: 0.8565346956151554 |
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- name: Recall |
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type: recall |
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value: 0.8565346956151554 |
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- name: F1 |
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type: f1 |
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value: 0.853731165851545 |
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- name: Precision |
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type: precision |
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value: 0.8631033150629456 |
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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-tiny-patch4-window8-256-finetuned-ind-17-imbalanced-aadhaarmask |
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This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-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: 0.3601 |
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- Accuracy: 0.8565 |
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- Recall: 0.8565 |
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- F1: 0.8537 |
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- Precision: 0.8631 |
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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 | 0.6645 | 0.7820 | 0.7820 | 0.7661 | 0.7678 | |
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| No log | 1.9983 | 587 | 0.5493 | 0.8033 | 0.8033 | 0.7897 | 0.7964 | |
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| No log | 2.9991 | 881 | 0.4242 | 0.8416 | 0.8416 | 0.8380 | 0.8460 | |
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| No log | 4.0 | 1175 | 0.4124 | 0.8310 | 0.8310 | 0.8288 | 0.8299 | |
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| No log | 4.9974 | 1468 | 0.3769 | 0.8412 | 0.8412 | 0.8388 | 0.8478 | |
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| No log | 5.9983 | 1762 | 0.3589 | 0.8501 | 0.8501 | 0.8481 | 0.8582 | |
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| No log | 6.9991 | 2056 | 0.3503 | 0.8455 | 0.8455 | 0.8456 | 0.8535 | |
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| No log | 8.0 | 2350 | 0.3400 | 0.8404 | 0.8404 | 0.8416 | 0.8465 | |
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| No log | 8.9974 | 2643 | 0.3533 | 0.8480 | 0.8480 | 0.8480 | 0.8501 | |
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| 0.5214 | 9.9745 | 2930 | 0.3358 | 0.8459 | 0.8459 | 0.8460 | 0.8473 | |
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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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