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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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- generated_from_trainer |
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datasets: |
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- imagefolder |
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- rajistics/indian_food_images |
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metrics: |
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- accuracy |
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widget: |
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- src: https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/003.jpg |
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example_title: Example1 |
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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: indian_food_images |
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type: imagefolder |
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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: 0.9521785334750266 |
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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 indian_food_images dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2139 |
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- Accuracy: 0.9522 |
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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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| 1.0846 | 0.3 | 100 | 0.9561 | 0.8555 | |
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| 0.7894 | 0.6 | 200 | 0.5871 | 0.8927 | |
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| 0.6233 | 0.9 | 300 | 0.4447 | 0.9107 | |
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| 0.3619 | 1.2 | 400 | 0.4355 | 0.8937 | |
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| 0.34 | 1.5 | 500 | 0.3712 | 0.9118 | |
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| 0.3413 | 1.8 | 600 | 0.4088 | 0.8916 | |
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| 0.3619 | 2.1 | 700 | 0.3741 | 0.9044 | |
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| 0.2135 | 2.4 | 800 | 0.3286 | 0.9160 | |
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| 0.2166 | 2.7 | 900 | 0.2758 | 0.9416 | |
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| 0.1557 | 3.0 | 1000 | 0.2679 | 0.9330 | |
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| 0.1115 | 3.3 | 1100 | 0.2529 | 0.9362 | |
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| 0.1571 | 3.6 | 1200 | 0.2360 | 0.9469 | |
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| 0.1079 | 3.9 | 1300 | 0.2139 | 0.9522 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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