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--- |
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license: apache-2.0 |
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base_model: microsoft/swinv2-large-patch4-window12-192-22k |
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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-window12-192-22k-augmented |
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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: 0.8723404255319149 |
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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-window12-192-22k-augmented |
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This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12-192-22k](https://huggingface.co./microsoft/swinv2-large-patch4-window12-192-22k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3067 |
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- Accuracy: 0.8723 |
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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: 0.0001 |
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- train_batch_size: 48 |
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- eval_batch_size: 48 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 384 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 0.89 | 3 | 1.4847 | 0.5816 | |
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| No log | 1.78 | 6 | 0.9256 | 0.6950 | |
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| 1.2457 | 2.96 | 10 | 0.6017 | 0.7589 | |
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| 1.2457 | 3.85 | 13 | 0.3806 | 0.8723 | |
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| 1.2457 | 4.74 | 16 | 0.3866 | 0.8440 | |
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| 0.3656 | 5.93 | 20 | 0.3358 | 0.8794 | |
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| 0.3656 | 6.81 | 23 | 0.2803 | 0.8865 | |
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| 0.3656 | 8.0 | 27 | 0.3079 | 0.8723 | |
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| 0.2205 | 8.89 | 30 | 0.3067 | 0.8723 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.1+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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