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--- |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: msi-swinv2-tiny |
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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: 0.6404109589041096 |
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- name: F1 |
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type: f1 |
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value: 0.5016949152542373 |
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- name: Precision |
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type: precision |
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value: 0.6290224650880388 |
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- name: Recall |
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type: recall |
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value: 0.41723721304873135 |
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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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# msi-swinv2-tiny |
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This model was trained from scratch on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7208 |
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- Accuracy: 0.6404 |
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- F1: 0.5017 |
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- Precision: 0.6290 |
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- Recall: 0.4172 |
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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: 1e-06 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.4992 | 1.0 | 2015 | 0.7072 | 0.6189 | 0.4517 | 0.6009 | 0.3619 | |
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| 0.4581 | 2.0 | 4031 | 0.7145 | 0.6383 | 0.4787 | 0.6387 | 0.3828 | |
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| 0.4229 | 3.0 | 6047 | 0.7146 | 0.6434 | 0.5077 | 0.6329 | 0.4238 | |
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| 0.4096 | 4.0 | 8060 | 0.7208 | 0.6404 | 0.5017 | 0.6290 | 0.4172 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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