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vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_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: 37.0129
- Accuracy: 0.8277
- Brier Loss: 0.3307
- Nll: 1.8775
- F1 Micro: 0.8277
- F1 Macro: 0.8289
- Ece: 0.1649
- Aurc: 0.0944
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: 64
- eval_batch_size: 64
- 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: 50
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
---|---|---|---|---|---|---|---|---|---|---|
No log | 1.0 | 250 | 56.2115 | 0.3142 | 0.8385 | 3.5992 | 0.3142 | 0.2499 | 0.1012 | 0.5692 |
56.615 | 2.0 | 500 | 54.0327 | 0.4025 | 0.9176 | 3.1629 | 0.4025 | 0.3116 | 0.4002 | 0.3781 |
56.615 | 3.0 | 750 | 49.9569 | 0.4728 | 0.8906 | 2.8997 | 0.4728 | 0.4076 | 0.4129 | 0.2864 |
50.7474 | 4.0 | 1000 | 47.4945 | 0.5685 | 0.7670 | 2.6755 | 0.5685 | 0.5350 | 0.3561 | 0.2844 |
50.7474 | 5.0 | 1250 | 45.5054 | 0.6378 | 0.6629 | 2.5408 | 0.6378 | 0.6030 | 0.3212 | 0.1851 |
45.4907 | 6.0 | 1500 | 43.9471 | 0.679 | 0.5949 | 2.6322 | 0.679 | 0.6636 | 0.2925 | 0.1474 |
45.4907 | 7.0 | 1750 | 42.9273 | 0.7342 | 0.4843 | 2.4382 | 0.7342 | 0.7365 | 0.2245 | 0.1436 |
42.5191 | 8.0 | 2000 | 41.9715 | 0.7548 | 0.4560 | 2.3596 | 0.7548 | 0.7533 | 0.2231 | 0.1400 |
42.5191 | 9.0 | 2250 | 41.4349 | 0.7722 | 0.4310 | 2.3144 | 0.7722 | 0.7718 | 0.2103 | 0.1304 |
40.8849 | 10.0 | 2500 | 41.0961 | 0.7805 | 0.4187 | 2.2268 | 0.7805 | 0.7826 | 0.2047 | 0.1305 |
40.8849 | 11.0 | 2750 | 40.5831 | 0.7893 | 0.4030 | 2.1663 | 0.7893 | 0.7930 | 0.2001 | 0.1246 |
39.8394 | 12.0 | 3000 | 40.1596 | 0.7987 | 0.3877 | 2.1719 | 0.7987 | 0.8015 | 0.1929 | 0.1162 |
39.8394 | 13.0 | 3250 | 39.8469 | 0.8033 | 0.3821 | 2.1455 | 0.8033 | 0.8077 | 0.1889 | 0.1183 |
38.9442 | 14.0 | 3500 | 39.5865 | 0.8055 | 0.3761 | 2.1121 | 0.8055 | 0.8096 | 0.1864 | 0.1110 |
38.9442 | 15.0 | 3750 | 39.4686 | 0.81 | 0.3693 | 2.0948 | 0.81 | 0.8125 | 0.1831 | 0.1114 |
38.3612 | 16.0 | 4000 | 39.1387 | 0.8207 | 0.3446 | 1.9957 | 0.8207 | 0.8219 | 0.1716 | 0.1038 |
38.3612 | 17.0 | 4250 | 38.8950 | 0.8143 | 0.3575 | 2.0339 | 0.8143 | 0.8152 | 0.1781 | 0.1034 |
37.7855 | 18.0 | 4500 | 38.6442 | 0.8215 | 0.3442 | 1.9658 | 0.8215 | 0.8236 | 0.1718 | 0.1036 |
37.7855 | 19.0 | 4750 | 38.5218 | 0.8197 | 0.3477 | 1.9627 | 0.8197 | 0.8220 | 0.1735 | 0.1070 |
37.3649 | 20.0 | 5000 | 38.3474 | 0.8225 | 0.3413 | 1.9886 | 0.8225 | 0.8239 | 0.1710 | 0.1028 |
37.3649 | 21.0 | 5250 | 38.2377 | 0.8257 | 0.3358 | 1.9864 | 0.8257 | 0.8269 | 0.1674 | 0.0957 |
37.0326 | 22.0 | 5500 | 38.1089 | 0.824 | 0.3418 | 1.9404 | 0.824 | 0.8257 | 0.1678 | 0.0980 |
37.0326 | 23.0 | 5750 | 37.9861 | 0.8273 | 0.3339 | 1.9540 | 0.8273 | 0.8285 | 0.1664 | 0.0985 |
36.7372 | 24.0 | 6000 | 37.8397 | 0.8255 | 0.3376 | 1.9492 | 0.8255 | 0.8268 | 0.1685 | 0.0944 |
36.7372 | 25.0 | 6250 | 37.7772 | 0.8253 | 0.3370 | 1.9078 | 0.8253 | 0.8255 | 0.1669 | 0.0997 |
36.4341 | 26.0 | 6500 | 37.6550 | 0.828 | 0.3325 | 1.9388 | 0.828 | 0.8284 | 0.1647 | 0.0943 |
36.4341 | 27.0 | 6750 | 37.5873 | 0.8255 | 0.3364 | 1.9319 | 0.8255 | 0.8261 | 0.1680 | 0.0920 |
36.2152 | 28.0 | 7000 | 37.5052 | 0.825 | 0.3379 | 1.8945 | 0.825 | 0.8268 | 0.1681 | 0.0981 |
36.2152 | 29.0 | 7250 | 37.4586 | 0.8243 | 0.3361 | 1.9094 | 0.8243 | 0.8251 | 0.1692 | 0.0945 |
36.0128 | 30.0 | 7500 | 37.3730 | 0.8277 | 0.3304 | 1.9062 | 0.8277 | 0.8288 | 0.1657 | 0.0946 |
36.0128 | 31.0 | 7750 | 37.3309 | 0.8277 | 0.3309 | 1.9045 | 0.8277 | 0.8291 | 0.1660 | 0.0947 |
35.8486 | 32.0 | 8000 | 37.2620 | 0.8267 | 0.3323 | 1.8884 | 0.8267 | 0.8279 | 0.1652 | 0.0950 |
35.8486 | 33.0 | 8250 | 37.2147 | 0.8275 | 0.3308 | 1.9079 | 0.8275 | 0.8290 | 0.1654 | 0.0960 |
35.6854 | 34.0 | 8500 | 37.1911 | 0.831 | 0.3252 | 1.8935 | 0.831 | 0.8323 | 0.1613 | 0.0939 |
35.6854 | 35.0 | 8750 | 37.1523 | 0.8283 | 0.3301 | 1.8847 | 0.8283 | 0.8293 | 0.1644 | 0.0972 |
35.5758 | 36.0 | 9000 | 37.1315 | 0.8305 | 0.3252 | 1.8941 | 0.8305 | 0.8317 | 0.1627 | 0.0934 |
35.5758 | 37.0 | 9250 | 37.1184 | 0.8275 | 0.3320 | 1.8844 | 0.8275 | 0.8285 | 0.1654 | 0.0923 |
35.4911 | 38.0 | 9500 | 37.1149 | 0.827 | 0.3327 | 1.8885 | 0.827 | 0.8288 | 0.1668 | 0.0953 |
35.4911 | 39.0 | 9750 | 37.1067 | 0.8267 | 0.3323 | 1.8846 | 0.8267 | 0.8281 | 0.1659 | 0.0932 |
35.4248 | 40.0 | 10000 | 37.0792 | 0.8293 | 0.3294 | 1.8840 | 0.8293 | 0.8305 | 0.1633 | 0.0937 |
35.4248 | 41.0 | 10250 | 37.0798 | 0.8297 | 0.3288 | 1.8718 | 0.8297 | 0.8309 | 0.1639 | 0.0929 |
35.3648 | 42.0 | 10500 | 37.0635 | 0.8265 | 0.3351 | 1.8883 | 0.8265 | 0.8279 | 0.1680 | 0.0951 |
35.3648 | 43.0 | 10750 | 37.0470 | 0.828 | 0.3308 | 1.8746 | 0.828 | 0.8294 | 0.1656 | 0.0939 |
35.2961 | 44.0 | 11000 | 37.0305 | 0.8273 | 0.3321 | 1.8901 | 0.8273 | 0.8286 | 0.1657 | 0.0932 |
35.2961 | 45.0 | 11250 | 37.0261 | 0.8275 | 0.3315 | 1.8823 | 0.8275 | 0.8287 | 0.1650 | 0.0949 |
35.241 | 46.0 | 11500 | 37.0253 | 0.827 | 0.3311 | 1.8751 | 0.827 | 0.8283 | 0.1662 | 0.0940 |
35.241 | 47.0 | 11750 | 37.0200 | 0.8277 | 0.3321 | 1.8708 | 0.8277 | 0.8289 | 0.1653 | 0.0949 |
35.2059 | 48.0 | 12000 | 37.0165 | 0.8277 | 0.3305 | 1.8745 | 0.8277 | 0.8289 | 0.1650 | 0.0934 |
35.2059 | 49.0 | 12250 | 37.0130 | 0.8275 | 0.3312 | 1.8743 | 0.8275 | 0.8287 | 0.1655 | 0.0942 |
35.18 | 50.0 | 12500 | 37.0129 | 0.8277 | 0.3307 | 1.8775 | 0.8277 | 0.8289 | 0.1649 | 0.0944 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2