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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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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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--- |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0027 |
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- Accuracy: 0.9996 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.7056 | 0.1 | 100 | 0.5113 | 0.8881 | |
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| 0.3027 | 0.21 | 200 | 0.1280 | 0.9796 | |
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| 0.2823 | 0.31 | 300 | 0.1580 | 0.9656 | |
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| 0.3273 | 0.42 | 400 | 0.0879 | 0.9837 | |
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| 0.1808 | 0.52 | 500 | 0.0812 | 0.9822 | |
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| 0.2101 | 0.63 | 600 | 0.0339 | 0.9937 | |
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| 0.1495 | 0.73 | 700 | 0.0568 | 0.9833 | |
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| 0.1296 | 0.84 | 800 | 0.0629 | 0.9844 | |
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| 0.1462 | 0.94 | 900 | 0.0886 | 0.9733 | |
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| 0.0519 | 1.04 | 1000 | 0.0544 | 0.9870 | |
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| 0.3192 | 1.15 | 1100 | 0.0892 | 0.9726 | |
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| 0.158 | 1.25 | 1200 | 0.0632 | 0.98 | |
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| 0.0266 | 1.36 | 1300 | 0.0233 | 0.9944 | |
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| 0.1832 | 1.46 | 1400 | 0.0292 | 0.9930 | |
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| 0.1212 | 1.57 | 1500 | 0.0489 | 0.9852 | |
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| 0.0994 | 1.67 | 1600 | 0.0142 | 0.9974 | |
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| 0.0219 | 1.78 | 1700 | 0.0277 | 0.9930 | |
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| 0.0664 | 1.88 | 1800 | 0.0158 | 0.9974 | |
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| 0.0834 | 1.99 | 1900 | 0.0124 | 0.9978 | |
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| 0.1093 | 2.09 | 2000 | 0.0140 | 0.9974 | |
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| 0.1726 | 2.19 | 2100 | 0.0147 | 0.9963 | |
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| 0.0476 | 2.3 | 2200 | 0.0058 | 0.9993 | |
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| 0.0257 | 2.4 | 2300 | 0.0424 | 0.9911 | |
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| 0.0215 | 2.51 | 2400 | 0.0076 | 0.9989 | |
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| 0.0748 | 2.61 | 2500 | 0.0099 | 0.9974 | |
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| 0.0059 | 2.72 | 2600 | 0.0053 | 0.9993 | |
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| 0.0527 | 2.82 | 2700 | 0.0149 | 0.9963 | |
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| 0.0203 | 2.93 | 2800 | 0.0041 | 0.9993 | |
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| 0.0791 | 3.03 | 2900 | 0.0033 | 0.9989 | |
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| 0.0389 | 3.13 | 3000 | 0.0033 | 0.9989 | |
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| 0.0459 | 3.24 | 3100 | 0.0044 | 0.9989 | |
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| 0.0276 | 3.34 | 3200 | 0.0031 | 0.9996 | |
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| 0.0139 | 3.45 | 3300 | 0.0028 | 0.9996 | |
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| 0.0076 | 3.55 | 3400 | 0.0055 | 0.9985 | |
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| 0.0097 | 3.66 | 3500 | 0.0027 | 0.9996 | |
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| 0.0193 | 3.76 | 3600 | 0.0026 | 0.9996 | |
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| 0.0471 | 3.87 | 3700 | 0.0027 | 0.9996 | |
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| 0.0282 | 3.97 | 3800 | 0.0027 | 0.9996 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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