--- license: apache-2.0 base_model: facebook/convnextv2-tiny-1k-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: convnextv2-tiny-1k-224-finetuned-topwear results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8388888888888889 --- # convnextv2-tiny-1k-224-finetuned-topwear This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co./facebook/convnextv2-tiny-1k-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6478 - Accuracy: 0.8389 ## 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: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:--------:|:----:|:---------------:|:--------:| | 2.7006 | 0.9412 | 12 | 2.6782 | 0.1167 | | 2.6863 | 1.9608 | 25 | 2.6272 | 0.1611 | | 2.6437 | 2.9804 | 38 | 2.5389 | 0.2889 | | 2.4851 | 4.0 | 51 | 2.4116 | 0.4111 | | 2.3732 | 4.9412 | 63 | 2.2707 | 0.4889 | | 2.2546 | 5.9608 | 76 | 2.0710 | 0.5722 | | 2.1023 | 6.9804 | 89 | 1.8371 | 0.6167 | | 1.7115 | 8.0 | 102 | 1.6161 | 0.6111 | | 1.5295 | 8.9412 | 114 | 1.4381 | 0.6278 | | 1.3366 | 9.9608 | 127 | 1.2540 | 0.65 | | 1.0556 | 10.9804 | 140 | 1.1632 | 0.6611 | | 0.9657 | 12.0 | 153 | 1.0600 | 0.7 | | 0.8703 | 12.9412 | 165 | 0.9983 | 0.7222 | | 0.8007 | 13.9608 | 178 | 0.9474 | 0.7278 | | 0.6398 | 14.9804 | 191 | 0.8634 | 0.75 | | 0.6023 | 16.0 | 204 | 0.8527 | 0.7278 | | 0.583 | 16.9412 | 216 | 0.7928 | 0.7667 | | 0.5279 | 17.9608 | 229 | 0.7897 | 0.7833 | | 0.4643 | 18.9804 | 242 | 0.7886 | 0.7667 | | 0.4296 | 20.0 | 255 | 0.7329 | 0.7833 | | 0.41 | 20.9412 | 267 | 0.7317 | 0.7611 | | 0.3674 | 21.9608 | 280 | 0.7171 | 0.7667 | | 0.3285 | 22.9804 | 293 | 0.7005 | 0.7833 | | 0.2978 | 24.0 | 306 | 0.6576 | 0.7889 | | 0.293 | 24.9412 | 318 | 0.6450 | 0.8 | | 0.2724 | 25.9608 | 331 | 0.6765 | 0.7889 | | 0.2494 | 26.9804 | 344 | 0.6826 | 0.8056 | | 0.2504 | 28.0 | 357 | 0.6710 | 0.8056 | | 0.2332 | 28.9412 | 369 | 0.6667 | 0.7778 | | 0.2012 | 29.9608 | 382 | 0.7399 | 0.7944 | | 0.1866 | 30.9804 | 395 | 0.7311 | 0.7833 | | 0.2031 | 32.0 | 408 | 0.7077 | 0.7944 | | 0.1969 | 32.9412 | 420 | 0.7769 | 0.7667 | | 0.1968 | 33.9608 | 433 | 0.7666 | 0.7833 | | 0.1712 | 34.9804 | 446 | 0.6796 | 0.8 | | 0.1813 | 36.0 | 459 | 0.6654 | 0.8111 | | 0.1678 | 36.9412 | 471 | 0.6851 | 0.7889 | | 0.1461 | 37.9608 | 484 | 0.7054 | 0.7833 | | 0.1244 | 38.9804 | 497 | 0.7013 | 0.8056 | | 0.1329 | 40.0 | 510 | 0.6785 | 0.8 | | 0.1186 | 40.9412 | 522 | 0.7500 | 0.7778 | | 0.1397 | 41.9608 | 535 | 0.6819 | 0.8167 | | 0.1324 | 42.9804 | 548 | 0.6257 | 0.8111 | | 0.111 | 44.0 | 561 | 0.5939 | 0.8278 | | 0.1228 | 44.9412 | 573 | 0.6379 | 0.8222 | | 0.1085 | 45.9608 | 586 | 0.6789 | 0.8222 | | 0.1234 | 46.9804 | 599 | 0.6241 | 0.8278 | | 0.1129 | 48.0 | 612 | 0.7503 | 0.7889 | | 0.1197 | 48.9412 | 624 | 0.6862 | 0.7944 | | 0.0898 | 49.9608 | 637 | 0.6764 | 0.7889 | | 0.1057 | 50.9804 | 650 | 0.6339 | 0.8167 | | 0.0893 | 52.0 | 663 | 0.5828 | 0.85 | | 0.0736 | 52.9412 | 675 | 0.6573 | 0.8111 | | 0.0752 | 53.9608 | 688 | 0.6806 | 0.7944 | | 0.1127 | 54.9804 | 701 | 0.6222 | 0.8111 | | 0.1126 | 56.0 | 714 | 0.6305 | 0.8167 | | 0.0874 | 56.9412 | 726 | 0.6593 | 0.8111 | | 0.0806 | 57.9608 | 739 | 0.7006 | 0.8167 | | 0.0978 | 58.9804 | 752 | 0.6680 | 0.8056 | | 0.0875 | 60.0 | 765 | 0.6739 | 0.8167 | | 0.0722 | 60.9412 | 777 | 0.6341 | 0.8333 | | 0.0942 | 61.9608 | 790 | 0.6428 | 0.8 | | 0.0957 | 62.9804 | 803 | 0.6758 | 0.8 | | 0.0814 | 64.0 | 816 | 0.6104 | 0.8167 | | 0.077 | 64.9412 | 828 | 0.6226 | 0.8111 | | 0.1004 | 65.9608 | 841 | 0.6899 | 0.8056 | | 0.0697 | 66.9804 | 854 | 0.7105 | 0.8167 | | 0.0754 | 68.0 | 867 | 0.6751 | 0.8111 | | 0.0842 | 68.9412 | 879 | 0.6912 | 0.7833 | | 0.0684 | 69.9608 | 892 | 0.7235 | 0.8167 | | 0.0684 | 70.9804 | 905 | 0.5840 | 0.8278 | | 0.0705 | 72.0 | 918 | 0.6636 | 0.8222 | | 0.0681 | 72.9412 | 930 | 0.6787 | 0.8 | | 0.0906 | 73.9608 | 943 | 0.6243 | 0.8389 | | 0.0453 | 74.9804 | 956 | 0.6787 | 0.8222 | | 0.0874 | 76.0 | 969 | 0.6259 | 0.8278 | | 0.051 | 76.9412 | 981 | 0.6590 | 0.8278 | | 0.0858 | 77.9608 | 994 | 0.6307 | 0.8278 | | 0.0601 | 78.9804 | 1007 | 0.6042 | 0.8444 | | 0.0601 | 80.0 | 1020 | 0.5875 | 0.8389 | | 0.067 | 80.9412 | 1032 | 0.6078 | 0.8389 | | 0.0556 | 81.9608 | 1045 | 0.6007 | 0.8444 | | 0.0661 | 82.9804 | 1058 | 0.6062 | 0.8333 | | 0.0651 | 84.0 | 1071 | 0.6387 | 0.8111 | | 0.0546 | 84.9412 | 1083 | 0.6861 | 0.8167 | | 0.0827 | 85.9608 | 1096 | 0.6073 | 0.8389 | | 0.052 | 86.9804 | 1109 | 0.5935 | 0.85 | | 0.0524 | 88.0 | 1122 | 0.5899 | 0.8389 | | 0.066 | 88.9412 | 1134 | 0.5954 | 0.8444 | | 0.0617 | 89.9608 | 1147 | 0.6145 | 0.8444 | | 0.0572 | 90.9804 | 1160 | 0.6176 | 0.8444 | | 0.0719 | 92.0 | 1173 | 0.6406 | 0.8278 | | 0.0734 | 92.9412 | 1185 | 0.6485 | 0.8333 | | 0.0616 | 93.9608 | 1198 | 0.6198 | 0.8333 | | 0.0557 | 94.9804 | 1211 | 0.6167 | 0.8389 | | 0.0494 | 96.0 | 1224 | 0.6480 | 0.8444 | | 0.0587 | 96.9412 | 1236 | 0.6076 | 0.85 | | 0.052 | 97.9608 | 1249 | 0.6512 | 0.8389 | | 0.0383 | 98.9804 | 1262 | 0.6782 | 0.8333 | | 0.0499 | 100.0 | 1275 | 0.6542 | 0.8278 | | 0.0511 | 100.9412 | 1287 | 0.6795 | 0.8389 | | 0.0452 | 101.9608 | 1300 | 0.6740 | 0.8333 | | 0.0475 | 102.9804 | 1313 | 0.6616 | 0.8389 | | 0.0455 | 104.0 | 1326 | 0.6490 | 0.8278 | | 0.0486 | 104.9412 | 1338 | 0.6331 | 0.8333 | | 0.0585 | 105.9608 | 1351 | 0.6299 | 0.8333 | | 0.0549 | 106.9804 | 1364 | 0.6398 | 0.8278 | | 0.0436 | 108.0 | 1377 | 0.6338 | 0.8444 | | 0.0429 | 108.9412 | 1389 | 0.6459 | 0.8389 | | 0.0449 | 109.9608 | 1402 | 0.6470 | 0.8444 | | 0.0559 | 110.9804 | 1415 | 0.6463 | 0.8389 | | 0.0378 | 112.0 | 1428 | 0.6480 | 0.8389 | | 0.0476 | 112.9412 | 1440 | 0.6478 | 0.8389 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1