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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-15eph-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: 0.9144158415841585 |
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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-15eph-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: 0.2970 |
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- Accuracy: 0.9144 |
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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: 15.0 |
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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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| 3.8787 | 0.42 | 500 | 3.9971 | 0.7163 | |
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| 0.8429 | 0.84 | 1000 | 0.6450 | 0.8678 | |
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| 0.8561 | 1.27 | 1500 | 0.4160 | 0.8945 | |
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| 0.5777 | 1.69 | 2000 | 0.3664 | 0.9006 | |
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| 12.3601 | 2.11 | 2500 | 12.0328 | 0.9023 | |
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| 49.0606 | 2.54 | 3000 | 48.5000 | 0.8526 | |
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| 75.3173 | 2.96 | 3500 | 75.5341 | 0.6942 | |
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| 93.6153 | 3.38 | 4000 | 93.3091 | 0.5929 | |
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| 103.5744 | 3.8 | 4500 | 103.1211 | 0.5846 | |
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| 107.7701 | 4.23 | 5000 | 108.0755 | 0.5398 | |
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| 109.5736 | 4.65 | 5500 | 108.7624 | 0.5855 | |
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| 1.8028 | 5.07 | 6000 | 1.0960 | 0.8179 | |
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| 1.2549 | 5.49 | 6500 | 0.6560 | 0.8695 | |
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| 0.7199 | 5.92 | 7000 | 0.5619 | 0.8769 | |
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| 0.8874 | 6.34 | 7500 | 0.5151 | 0.8859 | |
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| 0.7429 | 6.76 | 8000 | 0.4830 | 0.8898 | |
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| 0.6759 | 7.19 | 8500 | 0.4681 | 0.8926 | |
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| 0.5352 | 7.61 | 9000 | 0.4360 | 0.8956 | |
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| 0.6021 | 8.03 | 9500 | 0.4202 | 0.8979 | |
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| 0.5617 | 8.45 | 10000 | 0.3940 | 0.9003 | |
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| 0.7235 | 8.88 | 10500 | 0.3915 | 0.9000 | |
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| 0.5323 | 9.3 | 11000 | 0.3793 | 0.9017 | |
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| 0.589 | 9.72 | 11500 | 0.3670 | 0.9051 | |
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| 0.425 | 10.14 | 12000 | 0.3615 | 0.9059 | |
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| 0.7103 | 10.57 | 12500 | 0.3479 | 0.9070 | |
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| 0.6251 | 10.99 | 13000 | 0.3472 | 0.9073 | |
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| 0.623 | 11.41 | 13500 | 0.3353 | 0.9088 | |
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| 0.6012 | 11.83 | 14000 | 0.3292 | 0.9098 | |
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| 0.4984 | 12.26 | 14500 | 0.3230 | 0.9112 | |
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| 0.4763 | 12.68 | 15000 | 0.3158 | 0.9109 | |
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| 0.3209 | 13.1 | 15500 | 0.3120 | 0.9123 | |
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| 0.4854 | 13.52 | 16000 | 0.3057 | 0.9126 | |
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| 0.5472 | 13.95 | 16500 | 0.3032 | 0.9134 | |
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| 0.3264 | 14.37 | 17000 | 0.3013 | 0.9134 | |
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| 0.4136 | 14.79 | 17500 | 0.2977 | 0.9141 | |
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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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