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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-1to2r1.5-epo10-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: 0.9183762376237624 |
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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-1to2r1.5-epo10-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: 0.2391 |
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- Accuracy: 0.9184 |
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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: 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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| 0.3011 | 0.42 | 500 | 0.1951 | 0.9124 | |
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| 0.2613 | 0.84 | 1000 | 0.1897 | 0.9139 | |
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| 100.1552 | 1.27 | 1500 | 99.5975 | 0.7445 | |
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| 162.0751 | 1.69 | 2000 | 162.5020 | 0.3512 | |
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| 1.061 | 2.11 | 2500 | 0.7523 | 0.8550 | |
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| 0.9728 | 2.54 | 3000 | 0.5263 | 0.8767 | |
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| 0.5851 | 2.96 | 3500 | 0.4599 | 0.8892 | |
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| 0.4668 | 3.38 | 4000 | 0.4064 | 0.8938 | |
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| 0.6967 | 3.8 | 4500 | 0.3814 | 0.8986 | |
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| 0.4928 | 4.23 | 5000 | 0.3522 | 0.9036 | |
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| 0.4893 | 4.65 | 5500 | 0.3562 | 0.9026 | |
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| 0.5421 | 5.07 | 6000 | 0.3182 | 0.9049 | |
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| 0.4405 | 5.49 | 6500 | 0.3112 | 0.9071 | |
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| 0.4423 | 5.92 | 7000 | 0.3012 | 0.9092 | |
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| 0.4143 | 6.34 | 7500 | 0.2958 | 0.9095 | |
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| 0.4997 | 6.76 | 8000 | 0.2796 | 0.9126 | |
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| 0.2448 | 7.19 | 8500 | 0.2747 | 0.9124 | |
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| 0.4468 | 7.61 | 9000 | 0.2699 | 0.9144 | |
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| 0.4163 | 8.03 | 9500 | 0.2583 | 0.9166 | |
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| 0.3651 | 8.45 | 10000 | 0.2567 | 0.9165 | |
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| 0.3946 | 8.88 | 10500 | 0.2489 | 0.9176 | |
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| 0.3196 | 9.3 | 11000 | 0.2444 | 0.9180 | |
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| 0.312 | 9.72 | 11500 | 0.2402 | 0.9172 | |
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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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