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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-food101-jpqd-1to2r1-epo7-finetuned-student |
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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: 0.9213069306930693 |
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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-food101-jpqd-1to2r1-epo7-finetuned-student |
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This model is a fine-tuned version of [skylord/swin-finetuned-food101](https://huggingface.co/skylord/swin-finetuned-food101) on the food101 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1947 |
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- Accuracy: 0.9213 |
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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: 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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- num_epochs: 7.0 |
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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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| 0.2342 | 0.42 | 500 | 0.1993 | 0.9099 | |
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| 0.2891 | 0.84 | 1000 | 0.1912 | 0.9137 | |
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| 67.4995 | 1.27 | 1500 | 66.4760 | 0.8035 | |
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| 109.8398 | 1.69 | 2000 | 109.5154 | 0.4499 | |
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| 0.6337 | 2.11 | 2500 | 0.4865 | 0.8826 | |
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| 0.6605 | 2.54 | 3000 | 0.3551 | 0.9013 | |
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| 0.4013 | 2.96 | 3500 | 0.3176 | 0.9044 | |
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| 0.3949 | 3.38 | 4000 | 0.2839 | 0.9079 | |
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| 0.4632 | 3.8 | 4500 | 0.2652 | 0.9118 | |
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| 0.3717 | 4.23 | 5000 | 0.2459 | 0.9147 | |
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| 0.3308 | 4.65 | 5500 | 0.2439 | 0.9159 | |
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| 0.4232 | 5.07 | 6000 | 0.2259 | 0.9169 | |
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| 0.3426 | 5.49 | 6500 | 0.2147 | 0.9199 | |
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| 0.331 | 5.92 | 7000 | 0.2086 | 0.9189 | |
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| 0.3032 | 6.34 | 7500 | 0.2036 | 0.9201 | |
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| 0.3393 | 6.76 | 8000 | 0.1978 | 0.9204 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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