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---
license: apache-2.0
base_model: WinKawaks/vit-tiny-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dit-base_tobacco-tiny_tobacco3482_simkd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dit-base_tobacco-tiny_tobacco3482_simkd
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7298
- Accuracy: 0.8
- Brier Loss: 0.3356
- Nll: 1.1950
- F1 Micro: 0.8000
- F1 Macro: 0.7677
- Ece: 0.2868
- Aurc: 0.0614
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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 | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 1.0 | 50 | 1.0044 | 0.11 | 0.8970 | 5.3755 | 0.11 | 0.0297 | 0.1810 | 0.9082 |
| No log | 2.0 | 100 | 0.9997 | 0.27 | 0.8946 | 5.6759 | 0.27 | 0.1038 | 0.2752 | 0.7229 |
| No log | 3.0 | 150 | 0.9946 | 0.345 | 0.8902 | 4.6234 | 0.345 | 0.1969 | 0.3377 | 0.6577 |
| No log | 4.0 | 200 | 0.9814 | 0.4 | 0.8686 | 3.0912 | 0.4000 | 0.2605 | 0.3687 | 0.3808 |
| No log | 5.0 | 250 | 0.9618 | 0.56 | 0.8277 | 2.9065 | 0.56 | 0.4439 | 0.4769 | 0.2239 |
| No log | 6.0 | 300 | 0.9225 | 0.58 | 0.7429 | 2.5647 | 0.58 | 0.4408 | 0.4561 | 0.1944 |
| No log | 7.0 | 350 | 0.8843 | 0.705 | 0.6414 | 2.4145 | 0.705 | 0.5531 | 0.4493 | 0.1261 |
| No log | 8.0 | 400 | 0.8627 | 0.685 | 0.5773 | 2.4171 | 0.685 | 0.5755 | 0.3710 | 0.1378 |
| No log | 9.0 | 450 | 0.8252 | 0.73 | 0.5158 | 1.6133 | 0.7300 | 0.6403 | 0.3706 | 0.1066 |
| 0.9306 | 10.0 | 500 | 0.8164 | 0.74 | 0.4861 | 1.9299 | 0.74 | 0.6672 | 0.3352 | 0.1090 |
| 0.9306 | 11.0 | 550 | 0.8350 | 0.67 | 0.5078 | 2.0291 | 0.67 | 0.6083 | 0.3271 | 0.1514 |
| 0.9306 | 12.0 | 600 | 0.8089 | 0.695 | 0.4680 | 1.6726 | 0.695 | 0.6065 | 0.3049 | 0.1040 |
| 0.9306 | 13.0 | 650 | 0.7847 | 0.78 | 0.4097 | 1.3710 | 0.78 | 0.7067 | 0.3090 | 0.0825 |
| 0.9306 | 14.0 | 700 | 0.7793 | 0.8 | 0.3952 | 1.4382 | 0.8000 | 0.7351 | 0.3189 | 0.0823 |
| 0.9306 | 15.0 | 750 | 0.7756 | 0.775 | 0.3979 | 1.2640 | 0.775 | 0.6997 | 0.2950 | 0.0835 |
| 0.9306 | 16.0 | 800 | 0.7888 | 0.765 | 0.3927 | 1.2499 | 0.765 | 0.6894 | 0.3175 | 0.0719 |
| 0.9306 | 17.0 | 850 | 0.7596 | 0.795 | 0.3603 | 1.1834 | 0.795 | 0.7250 | 0.2930 | 0.0673 |
| 0.9306 | 18.0 | 900 | 0.7581 | 0.795 | 0.3580 | 1.1902 | 0.795 | 0.7241 | 0.3104 | 0.0665 |
| 0.9306 | 19.0 | 950 | 0.7546 | 0.81 | 0.3547 | 1.1055 | 0.81 | 0.7583 | 0.3024 | 0.0621 |
| 0.7329 | 20.0 | 1000 | 0.7520 | 0.81 | 0.3547 | 1.1284 | 0.81 | 0.7533 | 0.3209 | 0.0581 |
| 0.7329 | 21.0 | 1050 | 0.7669 | 0.775 | 0.3906 | 1.3812 | 0.775 | 0.7502 | 0.3212 | 0.0794 |
| 0.7329 | 22.0 | 1100 | 0.7532 | 0.81 | 0.3591 | 1.0982 | 0.81 | 0.7836 | 0.3035 | 0.0708 |
| 0.7329 | 23.0 | 1150 | 0.7519 | 0.805 | 0.3643 | 1.0628 | 0.805 | 0.7742 | 0.2813 | 0.0732 |
| 0.7329 | 24.0 | 1200 | 0.7494 | 0.795 | 0.3614 | 1.1123 | 0.795 | 0.7618 | 0.2988 | 0.0699 |
| 0.7329 | 25.0 | 1250 | 0.7517 | 0.79 | 0.3696 | 1.0703 | 0.79 | 0.7606 | 0.3081 | 0.0800 |
| 0.7329 | 26.0 | 1300 | 0.7513 | 0.795 | 0.3629 | 1.1020 | 0.795 | 0.7769 | 0.2797 | 0.0722 |
| 0.7329 | 27.0 | 1350 | 0.7485 | 0.795 | 0.3552 | 1.0352 | 0.795 | 0.7671 | 0.2678 | 0.0684 |
| 0.7329 | 28.0 | 1400 | 0.7442 | 0.805 | 0.3471 | 1.0956 | 0.805 | 0.7706 | 0.2807 | 0.0630 |
| 0.7329 | 29.0 | 1450 | 0.7473 | 0.795 | 0.3592 | 1.1204 | 0.795 | 0.7685 | 0.2897 | 0.0722 |
| 0.6917 | 30.0 | 1500 | 0.7449 | 0.815 | 0.3482 | 1.0584 | 0.815 | 0.7862 | 0.2949 | 0.0629 |
| 0.6917 | 31.0 | 1550 | 0.7443 | 0.8 | 0.3512 | 1.1010 | 0.8000 | 0.7770 | 0.2954 | 0.0622 |
| 0.6917 | 32.0 | 1600 | 0.7454 | 0.785 | 0.3543 | 1.0994 | 0.785 | 0.7631 | 0.2957 | 0.0639 |
| 0.6917 | 33.0 | 1650 | 0.7421 | 0.815 | 0.3449 | 1.1826 | 0.815 | 0.7853 | 0.2996 | 0.0592 |
| 0.6917 | 34.0 | 1700 | 0.7454 | 0.79 | 0.3559 | 1.1000 | 0.79 | 0.7597 | 0.2964 | 0.0659 |
| 0.6917 | 35.0 | 1750 | 0.7418 | 0.815 | 0.3477 | 1.1616 | 0.815 | 0.7867 | 0.3133 | 0.0617 |
| 0.6917 | 36.0 | 1800 | 0.7425 | 0.815 | 0.3464 | 1.1274 | 0.815 | 0.7949 | 0.3173 | 0.0578 |
| 0.6917 | 37.0 | 1850 | 0.7421 | 0.8 | 0.3448 | 1.1909 | 0.8000 | 0.7732 | 0.2900 | 0.0639 |
| 0.6917 | 38.0 | 1900 | 0.7415 | 0.795 | 0.3471 | 1.1816 | 0.795 | 0.7594 | 0.2860 | 0.0655 |
| 0.6917 | 39.0 | 1950 | 0.7405 | 0.78 | 0.3502 | 1.1084 | 0.78 | 0.7491 | 0.2709 | 0.0650 |
| 0.6764 | 40.0 | 2000 | 0.7398 | 0.81 | 0.3457 | 1.1746 | 0.81 | 0.7797 | 0.2973 | 0.0603 |
| 0.6764 | 41.0 | 2050 | 0.7394 | 0.805 | 0.3437 | 1.1201 | 0.805 | 0.7764 | 0.2915 | 0.0626 |
| 0.6764 | 42.0 | 2100 | 0.7380 | 0.81 | 0.3420 | 1.0987 | 0.81 | 0.7861 | 0.2815 | 0.0583 |
| 0.6764 | 43.0 | 2150 | 0.7386 | 0.8 | 0.3437 | 1.1855 | 0.8000 | 0.7667 | 0.2804 | 0.0617 |
| 0.6764 | 44.0 | 2200 | 0.7398 | 0.795 | 0.3437 | 1.1138 | 0.795 | 0.7660 | 0.2719 | 0.0614 |
| 0.6764 | 45.0 | 2250 | 0.7384 | 0.805 | 0.3441 | 1.1100 | 0.805 | 0.7699 | 0.3065 | 0.0628 |
| 0.6764 | 46.0 | 2300 | 0.7389 | 0.79 | 0.3488 | 1.1079 | 0.79 | 0.7552 | 0.2615 | 0.0647 |
| 0.6764 | 47.0 | 2350 | 0.7368 | 0.8 | 0.3440 | 1.1095 | 0.8000 | 0.7698 | 0.2908 | 0.0624 |
| 0.6764 | 48.0 | 2400 | 0.7365 | 0.8 | 0.3452 | 1.0995 | 0.8000 | 0.7739 | 0.2838 | 0.0645 |
| 0.6764 | 49.0 | 2450 | 0.7365 | 0.8 | 0.3367 | 1.0442 | 0.8000 | 0.7712 | 0.2735 | 0.0585 |
| 0.6662 | 50.0 | 2500 | 0.7342 | 0.815 | 0.3379 | 1.1009 | 0.815 | 0.7815 | 0.2964 | 0.0584 |
| 0.6662 | 51.0 | 2550 | 0.7340 | 0.805 | 0.3358 | 1.0985 | 0.805 | 0.7723 | 0.2635 | 0.0593 |
| 0.6662 | 52.0 | 2600 | 0.7370 | 0.8 | 0.3429 | 1.1227 | 0.8000 | 0.7709 | 0.2841 | 0.0603 |
| 0.6662 | 53.0 | 2650 | 0.7325 | 0.81 | 0.3380 | 1.1110 | 0.81 | 0.7790 | 0.3022 | 0.0601 |
| 0.6662 | 54.0 | 2700 | 0.7320 | 0.8 | 0.3363 | 1.0621 | 0.8000 | 0.7647 | 0.2815 | 0.0607 |
| 0.6662 | 55.0 | 2750 | 0.7324 | 0.805 | 0.3321 | 0.9926 | 0.805 | 0.7693 | 0.2972 | 0.0600 |
| 0.6662 | 56.0 | 2800 | 0.7318 | 0.805 | 0.3364 | 1.0537 | 0.805 | 0.7681 | 0.2554 | 0.0612 |
| 0.6662 | 57.0 | 2850 | 0.7311 | 0.82 | 0.3355 | 1.1133 | 0.82 | 0.7862 | 0.2776 | 0.0594 |
| 0.6662 | 58.0 | 2900 | 0.7317 | 0.81 | 0.3331 | 1.0662 | 0.81 | 0.7797 | 0.2600 | 0.0579 |
| 0.6662 | 59.0 | 2950 | 0.7327 | 0.805 | 0.3382 | 1.1876 | 0.805 | 0.7735 | 0.2797 | 0.0621 |
| 0.6577 | 60.0 | 3000 | 0.7322 | 0.8 | 0.3356 | 1.1864 | 0.8000 | 0.7680 | 0.2797 | 0.0612 |
| 0.6577 | 61.0 | 3050 | 0.7327 | 0.795 | 0.3391 | 1.1347 | 0.795 | 0.7614 | 0.2883 | 0.0641 |
| 0.6577 | 62.0 | 3100 | 0.7315 | 0.815 | 0.3364 | 1.1227 | 0.815 | 0.7848 | 0.2681 | 0.0599 |
| 0.6577 | 63.0 | 3150 | 0.7316 | 0.805 | 0.3392 | 1.0608 | 0.805 | 0.7717 | 0.2742 | 0.0632 |
| 0.6577 | 64.0 | 3200 | 0.7313 | 0.82 | 0.3341 | 1.0601 | 0.82 | 0.7878 | 0.2950 | 0.0583 |
| 0.6577 | 65.0 | 3250 | 0.7322 | 0.805 | 0.3388 | 1.1837 | 0.805 | 0.7747 | 0.2806 | 0.0638 |
| 0.6577 | 66.0 | 3300 | 0.7311 | 0.805 | 0.3373 | 1.0157 | 0.805 | 0.7757 | 0.2880 | 0.0629 |
| 0.6577 | 67.0 | 3350 | 0.7310 | 0.805 | 0.3344 | 1.1878 | 0.805 | 0.7766 | 0.2499 | 0.0609 |
| 0.6577 | 68.0 | 3400 | 0.7326 | 0.805 | 0.3391 | 1.0847 | 0.805 | 0.7729 | 0.2824 | 0.0636 |
| 0.6577 | 69.0 | 3450 | 0.7302 | 0.805 | 0.3376 | 1.1932 | 0.805 | 0.7778 | 0.2789 | 0.0617 |
| 0.6528 | 70.0 | 3500 | 0.7305 | 0.81 | 0.3359 | 0.9988 | 0.81 | 0.7787 | 0.2769 | 0.0622 |
| 0.6528 | 71.0 | 3550 | 0.7300 | 0.81 | 0.3328 | 1.0833 | 0.81 | 0.7776 | 0.2914 | 0.0594 |
| 0.6528 | 72.0 | 3600 | 0.7300 | 0.81 | 0.3343 | 1.1426 | 0.81 | 0.7776 | 0.2843 | 0.0594 |
| 0.6528 | 73.0 | 3650 | 0.7285 | 0.805 | 0.3341 | 1.1237 | 0.805 | 0.7701 | 0.2723 | 0.0614 |
| 0.6528 | 74.0 | 3700 | 0.7303 | 0.81 | 0.3368 | 1.1928 | 0.81 | 0.7768 | 0.2926 | 0.0612 |
| 0.6528 | 75.0 | 3750 | 0.7290 | 0.805 | 0.3318 | 1.0669 | 0.805 | 0.7709 | 0.2810 | 0.0603 |
| 0.6528 | 76.0 | 3800 | 0.7316 | 0.8 | 0.3382 | 1.1392 | 0.8000 | 0.7687 | 0.2505 | 0.0636 |
| 0.6528 | 77.0 | 3850 | 0.7284 | 0.8 | 0.3337 | 1.1338 | 0.8000 | 0.7720 | 0.2677 | 0.0610 |
| 0.6528 | 78.0 | 3900 | 0.7303 | 0.805 | 0.3373 | 1.1969 | 0.805 | 0.7729 | 0.2745 | 0.0618 |
| 0.6528 | 79.0 | 3950 | 0.7297 | 0.805 | 0.3369 | 1.1970 | 0.805 | 0.7743 | 0.2731 | 0.0606 |
| 0.6489 | 80.0 | 4000 | 0.7296 | 0.795 | 0.3362 | 1.1328 | 0.795 | 0.7656 | 0.2620 | 0.0627 |
| 0.6489 | 81.0 | 4050 | 0.7295 | 0.805 | 0.3363 | 1.1358 | 0.805 | 0.7726 | 0.2540 | 0.0608 |
| 0.6489 | 82.0 | 4100 | 0.7290 | 0.795 | 0.3341 | 1.1389 | 0.795 | 0.7668 | 0.2661 | 0.0630 |
| 0.6489 | 83.0 | 4150 | 0.7289 | 0.8 | 0.3364 | 1.0597 | 0.8000 | 0.7678 | 0.2838 | 0.0615 |
| 0.6489 | 84.0 | 4200 | 0.7291 | 0.805 | 0.3351 | 1.1277 | 0.805 | 0.7743 | 0.2621 | 0.0608 |
| 0.6489 | 85.0 | 4250 | 0.7297 | 0.795 | 0.3353 | 1.1953 | 0.795 | 0.7668 | 0.2666 | 0.0622 |
| 0.6489 | 86.0 | 4300 | 0.7286 | 0.805 | 0.3339 | 1.1278 | 0.805 | 0.7735 | 0.2668 | 0.0608 |
| 0.6489 | 87.0 | 4350 | 0.7298 | 0.8 | 0.3361 | 1.1423 | 0.8000 | 0.7677 | 0.2613 | 0.0614 |
| 0.6489 | 88.0 | 4400 | 0.7296 | 0.805 | 0.3346 | 1.1927 | 0.805 | 0.7743 | 0.2789 | 0.0612 |
| 0.6489 | 89.0 | 4450 | 0.7299 | 0.8 | 0.3359 | 1.1950 | 0.8000 | 0.7686 | 0.2500 | 0.0613 |
| 0.6462 | 90.0 | 4500 | 0.7297 | 0.805 | 0.3354 | 1.1934 | 0.805 | 0.7743 | 0.2939 | 0.0613 |
| 0.6462 | 91.0 | 4550 | 0.7294 | 0.8 | 0.3353 | 1.1313 | 0.8000 | 0.7685 | 0.2808 | 0.0610 |
| 0.6462 | 92.0 | 4600 | 0.7297 | 0.805 | 0.3356 | 1.1349 | 0.805 | 0.7765 | 0.2668 | 0.0614 |
| 0.6462 | 93.0 | 4650 | 0.7298 | 0.8 | 0.3354 | 1.1954 | 0.8000 | 0.7685 | 0.2700 | 0.0613 |
| 0.6462 | 94.0 | 4700 | 0.7301 | 0.8 | 0.3362 | 1.1951 | 0.8000 | 0.7677 | 0.2722 | 0.0616 |
| 0.6462 | 95.0 | 4750 | 0.7299 | 0.805 | 0.3360 | 1.1957 | 0.805 | 0.7743 | 0.2619 | 0.0614 |
| 0.6462 | 96.0 | 4800 | 0.7299 | 0.805 | 0.3357 | 1.1946 | 0.805 | 0.7743 | 0.2892 | 0.0611 |
| 0.6462 | 97.0 | 4850 | 0.7297 | 0.8 | 0.3355 | 1.1954 | 0.8000 | 0.7686 | 0.2703 | 0.0613 |
| 0.6462 | 98.0 | 4900 | 0.7298 | 0.8 | 0.3359 | 1.1952 | 0.8000 | 0.7677 | 0.2892 | 0.0615 |
| 0.6462 | 99.0 | 4950 | 0.7298 | 0.8 | 0.3357 | 1.1951 | 0.8000 | 0.7677 | 0.2720 | 0.0614 |
| 0.645 | 100.0 | 5000 | 0.7298 | 0.8 | 0.3356 | 1.1950 | 0.8000 | 0.7677 | 0.2868 | 0.0614 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.2.0.dev20231112+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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