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--- |
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license: apache-2.0 |
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base_model: microsoft/swin-tiny-patch4-window7-224 |
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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: swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification |
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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: 0.9148418491484185 |
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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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# swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification |
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This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co./microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2418 |
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- Accuracy: 0.9148 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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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| 0.4375 | 0.9836 | 30 | 0.4385 | 0.8564 | |
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| 0.3408 | 2.0 | 61 | 0.2872 | 0.8978 | |
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| 0.3106 | 2.9836 | 91 | 0.2598 | 0.9100 | |
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| 0.3167 | 4.0 | 122 | 0.2609 | 0.9124 | |
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| 0.2533 | 4.9836 | 152 | 0.2426 | 0.9075 | |
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| 0.256 | 6.0 | 183 | 0.2372 | 0.9075 | |
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| 0.2492 | 6.9836 | 213 | 0.2418 | 0.9148 | |
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| 0.2364 | 8.0 | 244 | 0.2352 | 0.9051 | |
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| 0.2301 | 8.9836 | 274 | 0.2348 | 0.9075 | |
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| 0.2255 | 9.8361 | 300 | 0.2350 | 0.8978 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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