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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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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: indian_food_images |
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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: 0.9330499468650372 |
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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: fried_rice |
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- src: https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/126.jpg |
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example_title: paani_puri |
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- src: https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/401.jpg |
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example_title: chapati |
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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: 0.2632 |
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- Accuracy: 0.9330 |
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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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| 1.1794 | 0.3 | 100 | 0.9208 | 0.8565 | |
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| 0.6513 | 0.6 | 200 | 0.5410 | 0.8842 | |
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| 0.5904 | 0.9 | 300 | 0.4978 | 0.8799 | |
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| 0.4461 | 1.2 | 400 | 0.3669 | 0.9192 | |
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| 0.5633 | 1.5 | 500 | 0.4340 | 0.8842 | |
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| 0.2489 | 1.8 | 600 | 0.3355 | 0.9171 | |
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| 0.3171 | 2.1 | 700 | 0.3286 | 0.9192 | |
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| 0.3785 | 2.4 | 800 | 0.3232 | 0.9171 | |
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| 0.2278 | 2.7 | 900 | 0.3338 | 0.9192 | |
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| 0.0894 | 3.0 | 1000 | 0.2870 | 0.9245 | |
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| 0.2092 | 3.3 | 1100 | 0.2884 | 0.9288 | |
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| 0.1466 | 3.6 | 1200 | 0.2673 | 0.9320 | |
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| 0.1789 | 3.9 | 1300 | 0.2632 | 0.9330 | |
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### Framework versions |
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- Transformers 4.21.1 |
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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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