--- library_name: transformers license: apache-2.0 base_model: facebook/convnextv2-tiny-1k-224 tags: - image-classification - vision - generated_from_trainer metrics: - accuracy model-index: - name: convnextv2-tiny-1k-224-text results: [] --- # convnextv2-tiny-1k-224-text This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the davanstrien/zenodo-presentations-open-labels dataset. It achieves the following results on the evaluation set: - Loss: 0.4554 - Accuracy: 0.7874 ## 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: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 1337 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 200.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5242 | 1.0 | 23 | 0.4961 | 0.7559 | | 0.459 | 2.0 | 46 | 0.5001 | 0.7638 | | 0.4429 | 3.0 | 69 | 0.4554 | 0.7874 | | 0.4308 | 4.0 | 92 | 0.4924 | 0.7638 | | 0.4319 | 5.0 | 115 | 0.4673 | 0.7874 | | 0.4047 | 6.0 | 138 | 0.4930 | 0.7756 | | 0.425 | 7.0 | 161 | 0.4739 | 0.7795 | | 0.4102 | 8.0 | 184 | 0.5118 | 0.7598 | | 0.3959 | 9.0 | 207 | 0.5490 | 0.7480 | | 0.365 | 10.0 | 230 | 0.5261 | 0.7638 | | 0.4214 | 11.0 | 253 | 0.5089 | 0.7795 | | 0.3798 | 12.0 | 276 | 0.4711 | 0.7992 | | 0.3906 | 13.0 | 299 | 0.5035 | 0.7913 | | 0.3706 | 14.0 | 322 | 0.4933 | 0.7953 | | 0.3766 | 15.0 | 345 | 0.4973 | 0.7992 | | 0.3213 | 16.0 | 368 | 0.5221 | 0.7874 | | 0.329 | 17.0 | 391 | 0.5400 | 0.7835 | | 0.3427 | 18.0 | 414 | 0.5252 | 0.7913 | | 0.3472 | 19.0 | 437 | 0.6208 | 0.7441 | | 0.3424 | 20.0 | 460 | 0.5320 | 0.7795 | | 0.3016 | 21.0 | 483 | 0.5488 | 0.7795 | | 0.3033 | 22.0 | 506 | 0.5889 | 0.7480 | | 0.3083 | 23.0 | 529 | 0.6108 | 0.7638 | | 0.2772 | 24.0 | 552 | 0.5845 | 0.7480 | | 0.287 | 25.0 | 575 | 0.5242 | 0.8071 | | 0.2651 | 26.0 | 598 | 0.6276 | 0.7598 | | 0.2696 | 27.0 | 621 | 0.5649 | 0.7835 | | 0.2701 | 28.0 | 644 | 0.6103 | 0.7756 | | 0.2451 | 29.0 | 667 | 0.6207 | 0.7638 | | 0.2705 | 30.0 | 690 | 0.5990 | 0.7756 | | 0.2553 | 31.0 | 713 | 0.5962 | 0.7835 | | 0.2559 | 32.0 | 736 | 0.6681 | 0.7717 | | 0.2405 | 33.0 | 759 | 0.5917 | 0.7638 | | 0.2707 | 34.0 | 782 | 0.5906 | 0.7638 | | 0.3004 | 35.0 | 805 | 0.5905 | 0.7874 | | 0.2404 | 36.0 | 828 | 0.5914 | 0.7677 | | 0.242 | 37.0 | 851 | 0.7637 | 0.7638 | | 0.2221 | 38.0 | 874 | 0.7117 | 0.7598 | | 0.2196 | 39.0 | 897 | 0.6442 | 0.7835 | | 0.23 | 40.0 | 920 | 0.7011 | 0.7717 | | 0.2045 | 41.0 | 943 | 0.7822 | 0.7598 | | 0.2043 | 42.0 | 966 | 0.7339 | 0.7520 | | 0.2413 | 43.0 | 989 | 0.6917 | 0.7677 | | 0.2135 | 44.0 | 1012 | 0.6954 | 0.7717 | | 0.2194 | 45.0 | 1035 | 0.6729 | 0.7795 | | 0.211 | 46.0 | 1058 | 0.6841 | 0.7835 | | 0.2155 | 47.0 | 1081 | 0.7108 | 0.7677 | | 0.2231 | 48.0 | 1104 | 0.6758 | 0.7677 | | 0.2364 | 49.0 | 1127 | 0.7747 | 0.7520 | | 0.222 | 50.0 | 1150 | 0.7104 | 0.7638 | | 0.2018 | 51.0 | 1173 | 0.6885 | 0.7953 | | 0.219 | 52.0 | 1196 | 0.7609 | 0.7520 | | 0.1916 | 53.0 | 1219 | 0.8394 | 0.7677 | | 0.1767 | 54.0 | 1242 | 0.7910 | 0.7717 | | 0.236 | 55.0 | 1265 | 0.7601 | 0.7756 | | 0.1898 | 56.0 | 1288 | 0.7501 | 0.7717 | | 0.1876 | 57.0 | 1311 | 0.7492 | 0.7756 | | 0.1592 | 58.0 | 1334 | 0.7905 | 0.7638 | | 0.1772 | 59.0 | 1357 | 0.7411 | 0.7717 | | 0.1787 | 60.0 | 1380 | 0.8145 | 0.7795 | | 0.1782 | 61.0 | 1403 | 0.7721 | 0.7795 | | 0.1781 | 62.0 | 1426 | 0.8022 | 0.7835 | | 0.1884 | 63.0 | 1449 | 0.8630 | 0.7756 | | 0.1905 | 64.0 | 1472 | 0.7472 | 0.7953 | | 0.16 | 65.0 | 1495 | 0.7761 | 0.7874 | | 0.1619 | 66.0 | 1518 | 0.8586 | 0.7795 | | 0.1768 | 67.0 | 1541 | 0.7700 | 0.7835 | | 0.1395 | 68.0 | 1564 | 0.8326 | 0.7717 | | 0.1536 | 69.0 | 1587 | 0.8442 | 0.7756 | | 0.208 | 70.0 | 1610 | 0.9289 | 0.7677 | | 0.1783 | 71.0 | 1633 | 0.9022 | 0.7638 | | 0.1572 | 72.0 | 1656 | 0.8510 | 0.7677 | | 0.1349 | 73.0 | 1679 | 0.7962 | 0.7677 | | 0.148 | 74.0 | 1702 | 0.8641 | 0.7756 | | 0.1768 | 75.0 | 1725 | 0.9277 | 0.7677 | | 0.1833 | 76.0 | 1748 | 0.8663 | 0.7638 | | 0.1696 | 77.0 | 1771 | 0.8302 | 0.7756 | | 0.1577 | 78.0 | 1794 | 0.8576 | 0.7638 | | 0.1724 | 79.0 | 1817 | 0.8652 | 0.7598 | | 0.1525 | 80.0 | 1840 | 0.8567 | 0.7717 | | 0.158 | 81.0 | 1863 | 0.9139 | 0.7598 | | 0.1639 | 82.0 | 1886 | 0.9689 | 0.7520 | | 0.1424 | 83.0 | 1909 | 0.9698 | 0.7638 | | 0.1224 | 84.0 | 1932 | 1.0239 | 0.7717 | | 0.1765 | 85.0 | 1955 | 0.9072 | 0.7795 | | 0.1726 | 86.0 | 1978 | 0.9436 | 0.7520 | | 0.1584 | 87.0 | 2001 | 0.8775 | 0.7638 | | 0.164 | 88.0 | 2024 | 0.8592 | 0.7717 | | 0.1682 | 89.0 | 2047 | 0.9051 | 0.7638 | | 0.1455 | 90.0 | 2070 | 1.0020 | 0.7717 | | 0.1596 | 91.0 | 2093 | 0.9423 | 0.7677 | | 0.1667 | 92.0 | 2116 | 0.9586 | 0.7638 | | 0.132 | 93.0 | 2139 | 0.9890 | 0.7638 | | 0.1335 | 94.0 | 2162 | 0.9922 | 0.7717 | | 0.1538 | 95.0 | 2185 | 0.9534 | 0.7520 | | 0.1288 | 96.0 | 2208 | 1.0714 | 0.7480 | | 0.1661 | 97.0 | 2231 | 0.9950 | 0.7598 | | 0.1392 | 98.0 | 2254 | 0.9866 | 0.7520 | | 0.1413 | 99.0 | 2277 | 1.0638 | 0.7598 | | 0.1619 | 100.0 | 2300 | 1.0178 | 0.7598 | | 0.1537 | 101.0 | 2323 | 0.9892 | 0.7638 | | 0.137 | 102.0 | 2346 | 0.9524 | 0.7559 | | 0.1416 | 103.0 | 2369 | 1.0539 | 0.7402 | | 0.1477 | 104.0 | 2392 | 1.0825 | 0.7283 | | 0.1283 | 105.0 | 2415 | 1.0008 | 0.7520 | | 0.1498 | 106.0 | 2438 | 0.9702 | 0.7638 | | 0.1576 | 107.0 | 2461 | 1.0144 | 0.7677 | | 0.1433 | 108.0 | 2484 | 0.9457 | 0.7638 | | 0.1377 | 109.0 | 2507 | 0.9770 | 0.7677 | | 0.1163 | 110.0 | 2530 | 1.1386 | 0.7559 | | 0.1449 | 111.0 | 2553 | 1.0589 | 0.7559 | | 0.1475 | 112.0 | 2576 | 1.0110 | 0.7480 | | 0.1582 | 113.0 | 2599 | 0.9657 | 0.7677 | | 0.1291 | 114.0 | 2622 | 0.9563 | 0.7756 | | 0.1106 | 115.0 | 2645 | 1.1004 | 0.7480 | | 0.1339 | 116.0 | 2668 | 1.0327 | 0.7520 | | 0.1344 | 117.0 | 2691 | 1.0161 | 0.7520 | | 0.1433 | 118.0 | 2714 | 1.0312 | 0.7559 | | 0.1271 | 119.0 | 2737 | 1.0266 | 0.7598 | | 0.1222 | 120.0 | 2760 | 1.0119 | 0.7638 | | 0.1235 | 121.0 | 2783 | 1.0808 | 0.7520 | | 0.1311 | 122.0 | 2806 | 1.0612 | 0.7520 | | 0.1219 | 123.0 | 2829 | 1.1412 | 0.7520 | | 0.148 | 124.0 | 2852 | 1.0836 | 0.7402 | | 0.1076 | 125.0 | 2875 | 1.0629 | 0.7559 | | 0.1306 | 126.0 | 2898 | 1.0791 | 0.7362 | | 0.1153 | 127.0 | 2921 | 1.1495 | 0.7402 | | 0.1239 | 128.0 | 2944 | 1.1446 | 0.7520 | | 0.1533 | 129.0 | 2967 | 1.0818 | 0.7441 | | 0.136 | 130.0 | 2990 | 1.0558 | 0.7520 | | 0.1189 | 131.0 | 3013 | 1.0423 | 0.7520 | | 0.1247 | 132.0 | 3036 | 1.0581 | 0.7638 | | 0.1136 | 133.0 | 3059 | 1.0132 | 0.7717 | | 0.1492 | 134.0 | 3082 | 1.1127 | 0.7441 | | 0.1184 | 135.0 | 3105 | 1.1450 | 0.7402 | | 0.1122 | 136.0 | 3128 | 1.1063 | 0.7520 | | 0.1047 | 137.0 | 3151 | 1.1029 | 0.7441 | | 0.1285 | 138.0 | 3174 | 1.1563 | 0.7402 | | 0.1004 | 139.0 | 3197 | 1.1552 | 0.7362 | | 0.1285 | 140.0 | 3220 | 1.1097 | 0.7480 | | 0.1257 | 141.0 | 3243 | 1.1602 | 0.7402 | | 0.1075 | 142.0 | 3266 | 1.1912 | 0.7559 | | 0.1098 | 143.0 | 3289 | 1.1894 | 0.7520 | | 0.1148 | 144.0 | 3312 | 1.1551 | 0.7441 | | 0.1489 | 145.0 | 3335 | 1.1379 | 0.7441 | | 0.1461 | 146.0 | 3358 | 1.1726 | 0.7480 | | 0.1171 | 147.0 | 3381 | 1.1191 | 0.7441 | | 0.1262 | 148.0 | 3404 | 1.1662 | 0.7441 | | 0.1137 | 149.0 | 3427 | 1.1283 | 0.7480 | | 0.1118 | 150.0 | 3450 | 1.1388 | 0.7480 | | 0.1169 | 151.0 | 3473 | 1.1627 | 0.7520 | | 0.1021 | 152.0 | 3496 | 1.1821 | 0.7323 | | 0.1392 | 153.0 | 3519 | 1.1672 | 0.7323 | | 0.1111 | 154.0 | 3542 | 1.2136 | 0.7402 | | 0.1298 | 155.0 | 3565 | 1.1966 | 0.7402 | | 0.1114 | 156.0 | 3588 | 1.1382 | 0.7362 | | 0.09 | 157.0 | 3611 | 1.1460 | 0.7323 | | 0.1294 | 158.0 | 3634 | 1.1612 | 0.7441 | | 0.1186 | 159.0 | 3657 | 1.2204 | 0.7402 | | 0.1096 | 160.0 | 3680 | 1.2096 | 0.7441 | | 0.1107 | 161.0 | 3703 | 1.1822 | 0.7480 | | 0.1094 | 162.0 | 3726 | 1.1908 | 0.7480 | | 0.1112 | 163.0 | 3749 | 1.1647 | 0.7402 | | 0.1042 | 164.0 | 3772 | 1.2523 | 0.7441 | | 0.0993 | 165.0 | 3795 | 1.2040 | 0.7402 | | 0.105 | 166.0 | 3818 | 1.2296 | 0.7402 | | 0.1071 | 167.0 | 3841 | 1.2863 | 0.7480 | | 0.108 | 168.0 | 3864 | 1.2372 | 0.7441 | | 0.1076 | 169.0 | 3887 | 1.1872 | 0.7480 | | 0.1107 | 170.0 | 3910 | 1.2354 | 0.7323 | | 0.1012 | 171.0 | 3933 | 1.2105 | 0.7441 | | 0.0918 | 172.0 | 3956 | 1.2026 | 0.7441 | | 0.1043 | 173.0 | 3979 | 1.2925 | 0.7559 | | 0.1035 | 174.0 | 4002 | 1.2314 | 0.7402 | | 0.1101 | 175.0 | 4025 | 1.1943 | 0.7441 | | 0.1084 | 176.0 | 4048 | 1.2069 | 0.7362 | | 0.1247 | 177.0 | 4071 | 1.2303 | 0.7520 | | 0.1278 | 178.0 | 4094 | 1.2118 | 0.7480 | | 0.1117 | 179.0 | 4117 | 1.2213 | 0.7480 | | 0.1123 | 180.0 | 4140 | 1.2403 | 0.7480 | | 0.0918 | 181.0 | 4163 | 1.1987 | 0.7441 | | 0.0827 | 182.0 | 4186 | 1.2358 | 0.7441 | | 0.0814 | 183.0 | 4209 | 1.2608 | 0.7441 | | 0.0897 | 184.0 | 4232 | 1.2370 | 0.7441 | | 0.1321 | 185.0 | 4255 | 1.2317 | 0.7480 | | 0.1194 | 186.0 | 4278 | 1.2289 | 0.7441 | | 0.1154 | 187.0 | 4301 | 1.1964 | 0.7441 | | 0.0964 | 188.0 | 4324 | 1.2009 | 0.7441 | | 0.0903 | 189.0 | 4347 | 1.2123 | 0.7441 | | 0.1174 | 190.0 | 4370 | 1.2335 | 0.7441 | | 0.0846 | 191.0 | 4393 | 1.2399 | 0.7441 | | 0.1073 | 192.0 | 4416 | 1.2432 | 0.7441 | | 0.0892 | 193.0 | 4439 | 1.2604 | 0.7480 | | 0.1158 | 194.0 | 4462 | 1.2473 | 0.7480 | | 0.1153 | 195.0 | 4485 | 1.2267 | 0.7441 | | 0.1208 | 196.0 | 4508 | 1.2178 | 0.7441 | | 0.083 | 197.0 | 4531 | 1.2145 | 0.7480 | | 0.1331 | 198.0 | 4554 | 1.2215 | 0.7441 | | 0.0943 | 199.0 | 4577 | 1.2238 | 0.7441 | | 0.0926 | 200.0 | 4600 | 1.2236 | 0.7441 | ### Framework versions - Transformers 4.46.0.dev0 - Pytorch 2.4.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1