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--- |
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license: apache-2.0 |
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tags: |
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- image-classification |
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- vision |
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- generated_from_trainer |
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datasets: |
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- food101 |
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metrics: |
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- accuracy |
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model-index: |
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- name: swin-base-food101-jpqd-ov |
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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: food101 |
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type: food101 |
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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: 0.9060990099009901 |
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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-base-food101-jpqd-ov |
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It was compressed using [NNCF](https://github.com/openvinotoolkit/nncf) with [Optimum Intel](https://github.com/huggingface/optimum-intel#openvino) following the |
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JPQD image classification example. |
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This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the food101 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3396 |
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- Accuracy: 0.9061 |
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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: 16 |
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- eval_batch_size: 128 |
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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: 10.0 |
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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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| 2.2162 | 0.42 | 500 | 2.1111 | 0.7967 | |
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| 0.729 | 0.84 | 1000 | 0.5474 | 0.8773 | |
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| 0.7536 | 1.27 | 1500 | 0.3844 | 0.8984 | |
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| 0.4822 | 1.69 | 2000 | 0.3340 | 0.9043 | |
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| 12.2559 | 2.11 | 2500 | 12.0128 | 0.9033 | |
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| 48.7302 | 2.54 | 3000 | 48.3874 | 0.8681 | |
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| 75.1831 | 2.96 | 3500 | 75.3200 | 0.7183 | |
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| 93.5572 | 3.38 | 4000 | 93.4142 | 0.5939 | |
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| 103.798 | 3.8 | 4500 | 103.4427 | 0.5634 | |
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| 108.0993 | 4.23 | 5000 | 108.6461 | 0.5490 | |
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| 110.1265 | 4.65 | 5500 | 109.3663 | 0.5636 | |
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| 1.5584 | 5.07 | 6000 | 0.9255 | 0.8374 | |
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| 1.0883 | 5.49 | 6500 | 0.5841 | 0.8758 | |
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| 0.7024 | 5.92 | 7000 | 0.5055 | 0.8854 | |
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| 0.9033 | 6.34 | 7500 | 0.4639 | 0.8901 | |
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| 0.6901 | 6.76 | 8000 | 0.4360 | 0.8947 | |
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| 0.6114 | 7.19 | 8500 | 0.4080 | 0.8978 | |
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| 0.5102 | 7.61 | 9000 | 0.3911 | 0.9009 | |
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| 0.7154 | 8.03 | 9500 | 0.3747 | 0.9027 | |
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| 0.5621 | 8.45 | 10000 | 0.3622 | 0.9021 | |
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| 0.5262 | 8.88 | 10500 | 0.3554 | 0.9041 | |
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| 0.5442 | 9.3 | 11000 | 0.3462 | 0.9053 | |
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| 0.5615 | 9.72 | 11500 | 0.3416 | 0.9061 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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