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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: jpqd-swin-b-20eph-r1.00-s2e5-mock-main-merge-pr2 |
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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: 0.9179009900990099 |
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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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# jpqd-swin-b-20eph-r1.00-s2e5-mock-main-merge-pr2 |
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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: 0.2715 |
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- Accuracy: 0.9179 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20.0 |
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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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| 5.1452 | 0.42 | 500 | 5.4928 | 0.6440 | |
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| 0.9839 | 0.84 | 1000 | 0.7956 | 0.8580 | |
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| 0.8533 | 1.27 | 1500 | 0.4392 | 0.8911 | |
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| 0.6123 | 1.69 | 2000 | 0.3768 | 0.8983 | |
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| 12.3076 | 2.11 | 2500 | 12.0798 | 0.8953 | |
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| 49.301 | 2.54 | 3000 | 48.6292 | 0.8343 | |
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| 75.6345 | 2.96 | 3500 | 75.7027 | 0.6777 | |
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| 94.2556 | 3.38 | 4000 | 93.5852 | 0.5604 | |
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| 103.3226 | 3.8 | 4500 | 103.1255 | 0.5702 | |
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| 107.3423 | 4.23 | 5000 | 107.9250 | 0.5359 | |
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| 108.9013 | 4.65 | 5500 | 108.5225 | 0.5882 | |
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| 2.045 | 5.07 | 6000 | 1.1149 | 0.8154 | |
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| 1.3377 | 5.49 | 6500 | 0.6747 | 0.8665 | |
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| 0.7565 | 5.92 | 7000 | 0.5814 | 0.8765 | |
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| 0.7493 | 6.34 | 7500 | 0.5460 | 0.8840 | |
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| 0.7693 | 6.76 | 8000 | 0.5109 | 0.8851 | |
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| 0.6082 | 7.19 | 8500 | 0.4893 | 0.8895 | |
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| 0.7575 | 7.61 | 9000 | 0.4521 | 0.8943 | |
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| 0.7943 | 8.03 | 9500 | 0.4465 | 0.8941 | |
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| 0.5521 | 8.45 | 10000 | 0.4119 | 0.8967 | |
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| 0.6536 | 8.88 | 10500 | 0.4071 | 0.9010 | |
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| 0.5164 | 9.3 | 11000 | 0.3945 | 0.9010 | |
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| 0.6687 | 9.72 | 11500 | 0.3884 | 0.9030 | |
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| 0.4374 | 10.14 | 12000 | 0.3764 | 0.9040 | |
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| 0.7326 | 10.57 | 12500 | 0.3678 | 0.9060 | |
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| 0.6148 | 10.99 | 13000 | 0.3602 | 0.9057 | |
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| 0.6068 | 11.41 | 13500 | 0.3566 | 0.9075 | |
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| 0.6105 | 11.83 | 14000 | 0.3456 | 0.9074 | |
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| 0.5277 | 12.26 | 14500 | 0.3383 | 0.9107 | |
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| 0.5255 | 12.68 | 15000 | 0.3328 | 0.9097 | |
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| 0.4536 | 13.1 | 15500 | 0.3268 | 0.9108 | |
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| 0.5337 | 13.52 | 16000 | 0.3256 | 0.9107 | |
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| 0.5299 | 13.95 | 16500 | 0.3161 | 0.9124 | |
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| 0.3037 | 14.37 | 17000 | 0.3162 | 0.9123 | |
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| 0.4171 | 14.79 | 17500 | 0.3078 | 0.9124 | |
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| 0.5375 | 15.22 | 18000 | 0.3002 | 0.9116 | |
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| 0.2722 | 15.64 | 18500 | 0.2953 | 0.9134 | |
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| 0.3684 | 16.06 | 19000 | 0.2960 | 0.9137 | |
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| 0.4369 | 16.48 | 19500 | 0.2918 | 0.9150 | |
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| 0.3346 | 16.91 | 20000 | 0.2856 | 0.9171 | |
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| 0.3645 | 17.33 | 20500 | 0.2856 | 0.9162 | |
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| 0.4475 | 17.75 | 21000 | 0.2833 | 0.9157 | |
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| 0.2553 | 18.17 | 21500 | 0.2788 | 0.9167 | |
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| 0.5098 | 18.6 | 22000 | 0.2766 | 0.9164 | |
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| 0.4149 | 19.02 | 22500 | 0.2732 | 0.9177 | |
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| 0.3737 | 19.44 | 23000 | 0.2734 | 0.9181 | |
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| 0.325 | 19.86 | 23500 | 0.2715 | 0.9176 | |
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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 2.10.1 |
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- Tokenizers 0.13.2 |
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