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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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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: finetuned-indian-food |
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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: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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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: 0.9543039319872476 |
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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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# finetuned-indian-food |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1918 |
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- Accuracy: 0.9543 |
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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: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 4 |
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- mixed_precision_training: Native AMP |
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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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| 1.0175 | 0.3 | 100 | 0.9247 | 0.8629 | |
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| 0.7418 | 0.6 | 200 | 0.5536 | 0.8990 | |
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| 0.6652 | 0.9 | 300 | 0.4036 | 0.9182 | |
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| 0.5959 | 1.2 | 400 | 0.4022 | 0.8980 | |
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| 0.4478 | 1.5 | 500 | 0.3247 | 0.9288 | |
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| 0.4717 | 1.8 | 600 | 0.3019 | 0.9267 | |
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| 0.34 | 2.1 | 700 | 0.2594 | 0.9352 | |
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| 0.3518 | 2.4 | 800 | 0.2507 | 0.9352 | |
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| 0.3352 | 2.7 | 900 | 0.2484 | 0.9426 | |
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| 0.2493 | 3.0 | 1000 | 0.2266 | 0.9394 | |
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| 0.2034 | 3.3 | 1100 | 0.2011 | 0.9479 | |
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| 0.1753 | 3.6 | 1200 | 0.2089 | 0.9447 | |
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| 0.1614 | 3.9 | 1300 | 0.1918 | 0.9543 | |
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### Framework versions |
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- Transformers 4.21.0 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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