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--- |
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license: apache-2.0 |
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base_model: microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft |
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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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model-index: |
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- name: swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes-merged |
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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: 0.7222222222222222 |
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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-large-patch4-window12to24-192to384-22kto1k-ft-microbes-merged |
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This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8645 |
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- Accuracy: 0.7222 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 3.8355 | 0.98 | 15 | 2.5831 | 0.3333 | |
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| 1.9292 | 1.97 | 30 | 1.6850 | 0.5046 | |
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| 1.4121 | 2.95 | 45 | 1.2324 | 0.5972 | |
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| 1.0121 | 4.0 | 61 | 1.0345 | 0.6852 | |
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| 0.854 | 4.98 | 76 | 0.9663 | 0.6806 | |
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| 0.701 | 5.97 | 91 | 0.9587 | 0.6991 | |
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| 0.5956 | 6.95 | 106 | 0.8626 | 0.7269 | |
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| 0.5713 | 7.87 | 120 | 0.8645 | 0.7222 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cpu |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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