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--- |
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license: apache-2.0 |
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tags: |
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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-finetuned-food101 |
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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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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9210297029702971 |
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- task: |
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type: image-classification |
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name: 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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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9135841584158416 |
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verified: true |
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- name: Precision Macro |
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type: precision |
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value: 0.9151645786633058 |
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verified: true |
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- name: Precision Micro |
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type: precision |
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value: 0.9135841584158416 |
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verified: true |
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- name: Precision Weighted |
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type: precision |
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value: 0.915164578663306 |
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verified: true |
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- name: Recall Macro |
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type: recall |
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value: 0.9135841584158414 |
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verified: true |
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- name: Recall Micro |
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type: recall |
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value: 0.9135841584158416 |
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verified: true |
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- name: Recall Weighted |
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type: recall |
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value: 0.9135841584158416 |
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verified: true |
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- name: F1 Macro |
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type: f1 |
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value: 0.9138785016966742 |
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verified: true |
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- name: F1 Micro |
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type: f1 |
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value: 0.9135841584158415 |
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verified: true |
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- name: F1 Weighted |
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type: f1 |
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value: 0.9138785016966743 |
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verified: true |
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- name: loss |
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type: loss |
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value: 0.30761435627937317 |
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verified: true |
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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-finetuned-food101 |
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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: 0.2772 |
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- Accuracy: 0.9210 |
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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: 16 |
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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: 3 |
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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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| 0.5077 | 1.0 | 1183 | 0.3851 | 0.8893 | |
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| 0.3523 | 2.0 | 2366 | 0.3124 | 0.9088 | |
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| 0.1158 | 3.0 | 3549 | 0.2772 | 0.9210 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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