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39-tiny_tobacco3482_og_simkd
This model is a fine-tuned version of WinKawaks/vit-tiny-patch16-224 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 239.0946
- Accuracy: 0.835
- Brier Loss: 0.3084
- Nll: 1.2679
- F1 Micro: 0.835
- F1 Macro: 0.8047
- Ece: 0.2467
- Aurc: 0.0448
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 | 246.9268 | 0.22 | 0.8877 | 7.2845 | 0.22 | 0.1442 | 0.2508 | 0.5734 |
| No log | 2.0 | 100 | 245.5493 | 0.355 | 0.7851 | 4.2469 | 0.3550 | 0.2408 | 0.2761 | 0.4302 |
| No log | 3.0 | 150 | 244.0258 | 0.58 | 0.6569 | 2.7197 | 0.58 | 0.4698 | 0.3335 | 0.2529 |
| No log | 4.0 | 200 | 243.8010 | 0.615 | 0.5800 | 3.1495 | 0.615 | 0.4701 | 0.2865 | 0.1927 |
| No log | 5.0 | 250 | 242.9670 | 0.64 | 0.5193 | 2.2510 | 0.64 | 0.4843 | 0.2774 | 0.1631 |
| No log | 6.0 | 300 | 243.1274 | 0.62 | 0.5595 | 2.8430 | 0.62 | 0.4881 | 0.2980 | 0.1558 |
| No log | 7.0 | 350 | 242.1450 | 0.675 | 0.4620 | 2.2479 | 0.675 | 0.5389 | 0.2322 | 0.1266 |
| No log | 8.0 | 400 | 242.5806 | 0.63 | 0.5222 | 2.6597 | 0.63 | 0.5539 | 0.2649 | 0.1584 |
| No log | 9.0 | 450 | 241.9880 | 0.755 | 0.4313 | 1.8660 | 0.755 | 0.6923 | 0.2780 | 0.1028 |
| 241.4671 | 10.0 | 500 | 241.9459 | 0.73 | 0.4274 | 1.9762 | 0.7300 | 0.6550 | 0.2539 | 0.0843 |
| 241.4671 | 11.0 | 550 | 241.4543 | 0.74 | 0.4410 | 1.7736 | 0.74 | 0.6867 | 0.2589 | 0.1197 |
| 241.4671 | 12.0 | 600 | 241.5311 | 0.79 | 0.3750 | 1.6579 | 0.79 | 0.7193 | 0.2754 | 0.0582 |
| 241.4671 | 13.0 | 650 | 241.6211 | 0.77 | 0.4231 | 1.4441 | 0.7700 | 0.7350 | 0.3134 | 0.0761 |
| 241.4671 | 14.0 | 700 | 241.2984 | 0.795 | 0.3898 | 1.5615 | 0.795 | 0.7650 | 0.2879 | 0.0721 |
| 241.4671 | 15.0 | 750 | 241.2752 | 0.81 | 0.3641 | 1.5167 | 0.81 | 0.7692 | 0.2956 | 0.0515 |
| 241.4671 | 16.0 | 800 | 240.9899 | 0.78 | 0.3821 | 1.7296 | 0.78 | 0.7434 | 0.2935 | 0.0688 |
| 241.4671 | 17.0 | 850 | 241.3208 | 0.81 | 0.3850 | 1.6487 | 0.81 | 0.7628 | 0.2946 | 0.0657 |
| 241.4671 | 18.0 | 900 | 241.0431 | 0.82 | 0.3678 | 1.5667 | 0.82 | 0.7701 | 0.3109 | 0.0504 |
| 241.4671 | 19.0 | 950 | 240.9436 | 0.815 | 0.3615 | 1.1826 | 0.815 | 0.7624 | 0.3019 | 0.0557 |
| 238.6853 | 20.0 | 1000 | 240.7993 | 0.83 | 0.3475 | 1.2078 | 0.83 | 0.7887 | 0.3062 | 0.0541 |
| 238.6853 | 21.0 | 1050 | 240.6124 | 0.825 | 0.3577 | 1.1927 | 0.825 | 0.7956 | 0.2883 | 0.0560 |
| 238.6853 | 22.0 | 1100 | 240.7105 | 0.815 | 0.3535 | 1.4564 | 0.815 | 0.7550 | 0.2814 | 0.0553 |
| 238.6853 | 23.0 | 1150 | 240.6886 | 0.815 | 0.3584 | 1.1556 | 0.815 | 0.7815 | 0.2993 | 0.0564 |
| 238.6853 | 24.0 | 1200 | 240.5978 | 0.84 | 0.3451 | 1.1351 | 0.8400 | 0.8002 | 0.2937 | 0.0485 |
| 238.6853 | 25.0 | 1250 | 240.3825 | 0.815 | 0.3453 | 1.2408 | 0.815 | 0.7918 | 0.2853 | 0.0525 |
| 238.6853 | 26.0 | 1300 | 240.2633 | 0.83 | 0.3431 | 1.3299 | 0.83 | 0.7883 | 0.2715 | 0.0562 |
| 238.6853 | 27.0 | 1350 | 240.5535 | 0.82 | 0.3475 | 1.4406 | 0.82 | 0.7874 | 0.2897 | 0.0536 |
| 238.6853 | 28.0 | 1400 | 240.3554 | 0.835 | 0.3447 | 1.2483 | 0.835 | 0.7947 | 0.3078 | 0.0464 |
| 238.6853 | 29.0 | 1450 | 240.2271 | 0.82 | 0.3274 | 1.5224 | 0.82 | 0.7862 | 0.2691 | 0.0467 |
| 237.7411 | 30.0 | 1500 | 240.2261 | 0.825 | 0.3388 | 1.2921 | 0.825 | 0.7939 | 0.2913 | 0.0485 |
| 237.7411 | 31.0 | 1550 | 240.4772 | 0.83 | 0.3474 | 1.3063 | 0.83 | 0.7823 | 0.2995 | 0.0516 |
| 237.7411 | 32.0 | 1600 | 240.2594 | 0.85 | 0.3509 | 1.3993 | 0.85 | 0.8184 | 0.3142 | 0.0496 |
| 237.7411 | 33.0 | 1650 | 239.9501 | 0.805 | 0.3413 | 1.2018 | 0.805 | 0.7630 | 0.2619 | 0.0592 |
| 237.7411 | 34.0 | 1700 | 240.2149 | 0.86 | 0.3409 | 1.2633 | 0.8600 | 0.8490 | 0.3229 | 0.0467 |
| 237.7411 | 35.0 | 1750 | 240.0215 | 0.82 | 0.3264 | 1.1435 | 0.82 | 0.7705 | 0.2538 | 0.0499 |
| 237.7411 | 36.0 | 1800 | 239.9490 | 0.85 | 0.3204 | 1.4364 | 0.85 | 0.8147 | 0.2752 | 0.0432 |
| 237.7411 | 37.0 | 1850 | 239.9360 | 0.825 | 0.3262 | 1.1487 | 0.825 | 0.7921 | 0.2706 | 0.0482 |
| 237.7411 | 38.0 | 1900 | 240.0647 | 0.855 | 0.3322 | 1.1655 | 0.855 | 0.8339 | 0.3053 | 0.0460 |
| 237.7411 | 39.0 | 1950 | 239.7761 | 0.825 | 0.3237 | 1.2252 | 0.825 | 0.7862 | 0.2732 | 0.0499 |
| 237.1023 | 40.0 | 2000 | 239.9426 | 0.83 | 0.3220 | 1.2096 | 0.83 | 0.7906 | 0.2748 | 0.0457 |
| 237.1023 | 41.0 | 2050 | 239.8765 | 0.835 | 0.3241 | 1.3547 | 0.835 | 0.7929 | 0.2661 | 0.0462 |
| 237.1023 | 42.0 | 2100 | 239.8423 | 0.835 | 0.3329 | 1.2423 | 0.835 | 0.8000 | 0.2955 | 0.0527 |
| 237.1023 | 43.0 | 2150 | 239.6674 | 0.83 | 0.3215 | 1.1217 | 0.83 | 0.8069 | 0.2597 | 0.0486 |
| 237.1023 | 44.0 | 2200 | 239.7237 | 0.81 | 0.3206 | 1.1322 | 0.81 | 0.7724 | 0.2500 | 0.0473 |
| 237.1023 | 45.0 | 2250 | 239.7981 | 0.82 | 0.3263 | 1.4167 | 0.82 | 0.7754 | 0.2786 | 0.0485 |
| 237.1023 | 46.0 | 2300 | 239.4825 | 0.83 | 0.3149 | 1.2610 | 0.83 | 0.7879 | 0.2436 | 0.0487 |
| 237.1023 | 47.0 | 2350 | 239.6016 | 0.83 | 0.3195 | 1.2602 | 0.83 | 0.7903 | 0.2631 | 0.0529 |
| 237.1023 | 48.0 | 2400 | 239.6693 | 0.84 | 0.3248 | 1.2935 | 0.8400 | 0.7992 | 0.2581 | 0.0505 |
| 237.1023 | 49.0 | 2450 | 239.6932 | 0.845 | 0.3235 | 1.1698 | 0.845 | 0.8024 | 0.2600 | 0.0459 |
| 236.6301 | 50.0 | 2500 | 239.5297 | 0.845 | 0.3151 | 1.4464 | 0.845 | 0.8057 | 0.2775 | 0.0467 |
| 236.6301 | 51.0 | 2550 | 239.6262 | 0.83 | 0.3236 | 1.2198 | 0.83 | 0.7879 | 0.2680 | 0.0466 |
| 236.6301 | 52.0 | 2600 | 239.4326 | 0.855 | 0.3111 | 1.2320 | 0.855 | 0.8338 | 0.2785 | 0.0452 |
| 236.6301 | 53.0 | 2650 | 239.5918 | 0.825 | 0.3171 | 1.2295 | 0.825 | 0.7966 | 0.2517 | 0.0440 |
| 236.6301 | 54.0 | 2700 | 239.5557 | 0.845 | 0.3215 | 1.1543 | 0.845 | 0.8027 | 0.2723 | 0.0453 |
| 236.6301 | 55.0 | 2750 | 239.4767 | 0.83 | 0.3113 | 1.0283 | 0.83 | 0.8107 | 0.2457 | 0.0446 |
| 236.6301 | 56.0 | 2800 | 239.3879 | 0.85 | 0.3112 | 1.2353 | 0.85 | 0.8146 | 0.2547 | 0.0449 |
| 236.6301 | 57.0 | 2850 | 239.3733 | 0.85 | 0.3104 | 1.3730 | 0.85 | 0.8236 | 0.2593 | 0.0451 |
| 236.6301 | 58.0 | 2900 | 239.4988 | 0.835 | 0.3174 | 1.3295 | 0.835 | 0.7984 | 0.2572 | 0.0468 |
| 236.6301 | 59.0 | 2950 | 239.3514 | 0.815 | 0.3103 | 1.2168 | 0.815 | 0.7746 | 0.2382 | 0.0453 |
| 236.2404 | 60.0 | 3000 | 239.3031 | 0.835 | 0.3083 | 1.2398 | 0.835 | 0.7944 | 0.2577 | 0.0479 |
| 236.2404 | 61.0 | 3050 | 239.3242 | 0.855 | 0.3095 | 1.2921 | 0.855 | 0.8307 | 0.2739 | 0.0438 |
| 236.2404 | 62.0 | 3100 | 239.3217 | 0.815 | 0.3135 | 1.2385 | 0.815 | 0.7799 | 0.2559 | 0.0481 |
| 236.2404 | 63.0 | 3150 | 239.2695 | 0.835 | 0.3102 | 1.0724 | 0.835 | 0.8068 | 0.2276 | 0.0471 |
| 236.2404 | 64.0 | 3200 | 239.3596 | 0.83 | 0.3124 | 1.1534 | 0.83 | 0.7911 | 0.2440 | 0.0442 |
| 236.2404 | 65.0 | 3250 | 239.2498 | 0.825 | 0.3097 | 1.2191 | 0.825 | 0.7804 | 0.2450 | 0.0459 |
| 236.2404 | 66.0 | 3300 | 239.1448 | 0.82 | 0.3020 | 1.0196 | 0.82 | 0.7825 | 0.2559 | 0.0452 |
| 236.2404 | 67.0 | 3350 | 239.1730 | 0.825 | 0.3017 | 1.1825 | 0.825 | 0.8065 | 0.2494 | 0.0444 |
| 236.2404 | 68.0 | 3400 | 239.3466 | 0.835 | 0.3159 | 1.2392 | 0.835 | 0.7973 | 0.2617 | 0.0448 |
| 236.2404 | 69.0 | 3450 | 239.1867 | 0.825 | 0.3058 | 1.1611 | 0.825 | 0.7940 | 0.2596 | 0.0450 |
| 235.9626 | 70.0 | 3500 | 239.2792 | 0.84 | 0.3106 | 1.1812 | 0.8400 | 0.8134 | 0.2590 | 0.0444 |
| 235.9626 | 71.0 | 3550 | 239.0603 | 0.82 | 0.3055 | 1.1710 | 0.82 | 0.7895 | 0.2251 | 0.0466 |
| 235.9626 | 72.0 | 3600 | 238.9947 | 0.815 | 0.3069 | 1.1766 | 0.815 | 0.7905 | 0.2315 | 0.0485 |
| 235.9626 | 73.0 | 3650 | 239.2322 | 0.83 | 0.3108 | 1.1724 | 0.83 | 0.8015 | 0.2595 | 0.0450 |
| 235.9626 | 74.0 | 3700 | 239.0134 | 0.825 | 0.3087 | 1.2661 | 0.825 | 0.7854 | 0.2352 | 0.0467 |
| 235.9626 | 75.0 | 3750 | 239.1055 | 0.825 | 0.3090 | 1.1748 | 0.825 | 0.7946 | 0.2483 | 0.0458 |
| 235.9626 | 76.0 | 3800 | 239.0925 | 0.825 | 0.3133 | 1.1843 | 0.825 | 0.7918 | 0.2466 | 0.0490 |
| 235.9626 | 77.0 | 3850 | 239.1586 | 0.835 | 0.3115 | 1.1877 | 0.835 | 0.8140 | 0.2498 | 0.0455 |
| 235.9626 | 78.0 | 3900 | 239.1394 | 0.83 | 0.3103 | 1.2698 | 0.83 | 0.7897 | 0.2424 | 0.0467 |
| 235.9626 | 79.0 | 3950 | 239.2314 | 0.83 | 0.3121 | 1.2519 | 0.83 | 0.7938 | 0.2378 | 0.0453 |
| 235.7667 | 80.0 | 4000 | 239.1433 | 0.83 | 0.3076 | 1.1725 | 0.83 | 0.7924 | 0.2412 | 0.0459 |
| 235.7667 | 81.0 | 4050 | 239.0533 | 0.83 | 0.3026 | 1.2551 | 0.83 | 0.7994 | 0.2661 | 0.0448 |
| 235.7667 | 82.0 | 4100 | 239.0847 | 0.825 | 0.3123 | 1.1798 | 0.825 | 0.7964 | 0.2611 | 0.0483 |
| 235.7667 | 83.0 | 4150 | 239.1199 | 0.835 | 0.3089 | 1.2642 | 0.835 | 0.8047 | 0.2551 | 0.0471 |
| 235.7667 | 84.0 | 4200 | 239.0148 | 0.835 | 0.3055 | 1.1264 | 0.835 | 0.7975 | 0.2341 | 0.0439 |
| 235.7667 | 85.0 | 4250 | 239.0657 | 0.825 | 0.3107 | 1.1645 | 0.825 | 0.7924 | 0.2363 | 0.0452 |
| 235.7667 | 86.0 | 4300 | 239.1023 | 0.83 | 0.3134 | 1.2785 | 0.83 | 0.7949 | 0.2353 | 0.0462 |
| 235.7667 | 87.0 | 4350 | 239.0670 | 0.83 | 0.3028 | 1.2849 | 0.83 | 0.7927 | 0.2295 | 0.0428 |
| 235.7667 | 88.0 | 4400 | 239.0111 | 0.82 | 0.3097 | 1.1759 | 0.82 | 0.7865 | 0.2447 | 0.0462 |
| 235.7667 | 89.0 | 4450 | 238.9677 | 0.845 | 0.2995 | 1.1189 | 0.845 | 0.8231 | 0.2402 | 0.0423 |
| 235.6149 | 90.0 | 4500 | 239.1255 | 0.83 | 0.3063 | 1.1282 | 0.83 | 0.8028 | 0.2695 | 0.0431 |
| 235.6149 | 91.0 | 4550 | 238.9880 | 0.815 | 0.3102 | 1.1766 | 0.815 | 0.7851 | 0.2388 | 0.0486 |
| 235.6149 | 92.0 | 4600 | 239.0212 | 0.84 | 0.3079 | 1.1941 | 0.8400 | 0.8121 | 0.2627 | 0.0442 |
| 235.6149 | 93.0 | 4650 | 238.9948 | 0.82 | 0.3077 | 1.1702 | 0.82 | 0.7874 | 0.2533 | 0.0468 |
| 235.6149 | 94.0 | 4700 | 239.0357 | 0.82 | 0.3103 | 1.1823 | 0.82 | 0.7878 | 0.2639 | 0.0471 |
| 235.6149 | 95.0 | 4750 | 238.9936 | 0.81 | 0.2995 | 1.1251 | 0.81 | 0.7738 | 0.2519 | 0.0447 |
| 235.6149 | 96.0 | 4800 | 239.1319 | 0.835 | 0.3138 | 1.1957 | 0.835 | 0.8045 | 0.2806 | 0.0455 |
| 235.6149 | 97.0 | 4850 | 239.0160 | 0.815 | 0.3061 | 1.1852 | 0.815 | 0.7756 | 0.2281 | 0.0453 |
| 235.6149 | 98.0 | 4900 | 239.1168 | 0.83 | 0.3152 | 1.2637 | 0.83 | 0.7997 | 0.2509 | 0.0474 |
| 235.6149 | 99.0 | 4950 | 239.0691 | 0.83 | 0.3085 | 1.1799 | 0.83 | 0.7998 | 0.2524 | 0.0439 |
| 235.5384 | 100.0 | 5000 | 239.0946 | 0.835 | 0.3084 | 1.2679 | 0.835 | 0.8047 | 0.2467 | 0.0448 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
