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food_vit_model
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the food101 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5959
- Accuracy: 0.8749
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4008 | 1.0 | 592 | 0.5106 | 0.8690 |
| 0.313 | 2.0 | 1184 | 0.5045 | 0.8718 |
| 0.3444 | 3.0 | 1776 | 0.5029 | 0.8711 |
| 0.3305 | 4.0 | 2368 | 0.5049 | 0.8706 |
| 0.2993 | 5.0 | 2960 | 0.5123 | 0.8702 |
| 0.3658 | 6.0 | 3552 | 0.5202 | 0.8662 |
| 0.314 | 7.0 | 4144 | 0.5344 | 0.8633 |
| 0.2973 | 8.0 | 4736 | 0.5558 | 0.8589 |
| 0.3171 | 9.0 | 5328 | 0.5806 | 0.8566 |
| 0.2841 | 10.0 | 5920 | 0.5932 | 0.856 |
| 0.4034 | 11.0 | 6512 | 0.5770 | 0.8554 |
| 0.3231 | 12.0 | 7104 | 0.5455 | 0.8607 |
| 0.3162 | 13.0 | 7696 | 0.5420 | 0.8634 |
| 0.3706 | 14.0 | 8288 | 0.5591 | 0.8590 |
| 0.2857 | 15.0 | 8880 | 0.5284 | 0.8653 |
| 0.2647 | 16.0 | 9472 | 0.5680 | 0.8567 |
| 0.2411 | 17.0 | 10064 | 0.5492 | 0.8648 |
| 0.2566 | 18.0 | 10656 | 0.5716 | 0.8581 |
| 0.2338 | 19.0 | 11248 | 0.5842 | 0.8573 |
| 0.2862 | 20.0 | 11840 | 0.5735 | 0.8592 |
| 0.2689 | 21.0 | 12432 | 0.5669 | 0.8604 |
| 0.1892 | 22.0 | 13024 | 0.5747 | 0.8602 |
| 0.1801 | 23.0 | 13616 | 0.5581 | 0.8627 |
| 0.2258 | 24.0 | 14208 | 0.5717 | 0.8614 |
| 0.2215 | 25.0 | 14800 | 0.6046 | 0.8562 |
| 0.1443 | 26.0 | 15392 | 0.5758 | 0.8642 |
| 0.2143 | 27.0 | 15984 | 0.5805 | 0.8626 |
| 0.1699 | 28.0 | 16576 | 0.5843 | 0.8616 |
| 0.1787 | 29.0 | 17168 | 0.5740 | 0.8657 |
| 0.1702 | 30.0 | 17760 | 0.5718 | 0.8653 |
| 0.1703 | 31.0 | 18352 | 0.5703 | 0.8646 |
| 0.1692 | 32.0 | 18944 | 0.5918 | 0.8627 |
| 0.1643 | 33.0 | 19536 | 0.6041 | 0.8608 |
| 0.214 | 34.0 | 20128 | 0.5950 | 0.8624 |
| 0.1996 | 35.0 | 20720 | 0.5861 | 0.8637 |
| 0.1618 | 36.0 | 21312 | 0.6032 | 0.8622 |
| 0.181 | 37.0 | 21904 | 0.5915 | 0.8646 |
| 0.1641 | 38.0 | 22496 | 0.5697 | 0.8663 |
| 0.1233 | 39.0 | 23088 | 0.5987 | 0.8617 |
| 0.1469 | 40.0 | 23680 | 0.5944 | 0.8635 |
| 0.1492 | 41.0 | 24272 | 0.5893 | 0.8651 |
| 0.1616 | 42.0 | 24864 | 0.5717 | 0.8667 |
| 0.1359 | 43.0 | 25456 | 0.5897 | 0.8655 |
| 0.1318 | 44.0 | 26048 | 0.5920 | 0.8684 |
| 0.102 | 45.0 | 26640 | 0.5908 | 0.8683 |
| 0.1416 | 46.0 | 27232 | 0.5977 | 0.8625 |
| 0.1393 | 47.0 | 27824 | 0.6069 | 0.8648 |
| 0.1003 | 48.0 | 28416 | 0.5849 | 0.8682 |
| 0.121 | 49.0 | 29008 | 0.5880 | 0.8661 |
| 0.128 | 50.0 | 29600 | 0.5800 | 0.8693 |
| 0.1409 | 51.0 | 30192 | 0.6004 | 0.8663 |
| 0.1783 | 52.0 | 30784 | 0.5847 | 0.8678 |
| 0.1177 | 53.0 | 31376 | 0.5984 | 0.8683 |
| 0.097 | 54.0 | 31968 | 0.5973 | 0.8669 |
| 0.137 | 55.0 | 32560 | 0.5983 | 0.8668 |
| 0.1227 | 56.0 | 33152 | 0.5913 | 0.8689 |
| 0.1259 | 57.0 | 33744 | 0.5949 | 0.868 |
| 0.0947 | 58.0 | 34336 | 0.6065 | 0.8664 |
| 0.1184 | 59.0 | 34928 | 0.6098 | 0.8667 |
| 0.0996 | 60.0 | 35520 | 0.5958 | 0.8700 |
| 0.0977 | 61.0 | 36112 | 0.6019 | 0.8694 |
| 0.1295 | 62.0 | 36704 | 0.6012 | 0.8698 |
| 0.0842 | 63.0 | 37296 | 0.5993 | 0.8688 |
| 0.0784 | 64.0 | 37888 | 0.6074 | 0.8689 |
| 0.1183 | 65.0 | 38480 | 0.5853 | 0.8713 |
| 0.1215 | 66.0 | 39072 | 0.5962 | 0.8709 |
| 0.1069 | 67.0 | 39664 | 0.5786 | 0.8728 |
| 0.101 | 68.0 | 40256 | 0.5938 | 0.8691 |
| 0.1004 | 69.0 | 40848 | 0.5985 | 0.8716 |
| 0.0958 | 70.0 | 41440 | 0.5961 | 0.8721 |
| 0.0914 | 71.0 | 42032 | 0.6053 | 0.8704 |
| 0.0915 | 72.0 | 42624 | 0.5937 | 0.8713 |
| 0.0964 | 73.0 | 43216 | 0.6001 | 0.8703 |
| 0.0558 | 74.0 | 43808 | 0.5993 | 0.8697 |
| 0.0977 | 75.0 | 44400 | 0.6025 | 0.8706 |
| 0.1096 | 76.0 | 44992 | 0.6018 | 0.8706 |
| 0.0883 | 77.0 | 45584 | 0.5973 | 0.8733 |
| 0.0811 | 78.0 | 46176 | 0.6023 | 0.8741 |
| 0.0912 | 79.0 | 46768 | 0.6004 | 0.8733 |
| 0.0981 | 80.0 | 47360 | 0.5851 | 0.8730 |
| 0.0892 | 81.0 | 47952 | 0.5782 | 0.8754 |
| 0.1119 | 82.0 | 48544 | 0.5893 | 0.8727 |
| 0.1016 | 83.0 | 49136 | 0.5911 | 0.8722 |
| 0.0801 | 84.0 | 49728 | 0.5880 | 0.8755 |
| 0.107 | 85.0 | 50320 | 0.6088 | 0.8710 |
| 0.0763 | 86.0 | 50912 | 0.5912 | 0.8760 |
| 0.0667 | 87.0 | 51504 | 0.5974 | 0.8752 |
| 0.0485 | 88.0 | 52096 | 0.5903 | 0.8763 |
| 0.1002 | 89.0 | 52688 | 0.6097 | 0.8744 |
| 0.0786 | 90.0 | 53280 | 0.5853 | 0.8762 |
| 0.1067 | 91.0 | 53872 | 0.5874 | 0.8772 |
| 0.0618 | 92.0 | 54464 | 0.5847 | 0.8762 |
| 0.0667 | 93.0 | 55056 | 0.5803 | 0.8774 |
| 0.0702 | 94.0 | 55648 | 0.5812 | 0.8781 |
| 0.055 | 95.0 | 56240 | 0.5918 | 0.8761 |
| 0.0941 | 96.0 | 56832 | 0.5904 | 0.8766 |
| 0.0821 | 97.0 | 57424 | 0.5849 | 0.8762 |
| 0.0998 | 98.0 | 58016 | 0.5891 | 0.8757 |
| 0.0594 | 99.0 | 58608 | 0.5845 | 0.8778 |
| 0.0727 | 100.0 | 59200 | 0.5959 | 0.8749 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
