--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: visual-emotion-recognition 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: 0.6375 - name: Precision type: precision value: 0.6498416164333246 - name: Recall type: recall value: 0.6375 - name: F1 type: f1 value: 0.6340720916258936 --- # visual-emotion-recognition This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1334 - Accuracy: 0.6375 - Precision: 0.6498 - Recall: 0.6375 - F1: 0.6341 ## 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: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 48 - 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 2.0671 | 0.97 | 13 | 2.0660 | 0.125 | 0.2709 | 0.125 | 0.1135 | | 2.0576 | 1.95 | 26 | 2.0563 | 0.1562 | 0.2932 | 0.1562 | 0.1402 | | 2.044 | 3.0 | 40 | 2.0439 | 0.1875 | 0.2554 | 0.1875 | 0.1827 | | 2.0209 | 3.98 | 53 | 2.0309 | 0.2062 | 0.2405 | 0.2062 | 0.1961 | | 1.9938 | 4.95 | 66 | 2.0176 | 0.2188 | 0.2410 | 0.2188 | 0.2062 | | 1.9894 | 6.0 | 80 | 1.9960 | 0.2625 | 0.2700 | 0.2625 | 0.2438 | | 1.9667 | 6.97 | 93 | 1.9743 | 0.3125 | 0.3089 | 0.3125 | 0.2901 | | 1.9158 | 7.95 | 106 | 1.9421 | 0.3063 | 0.2557 | 0.3063 | 0.2687 | | 1.8834 | 9.0 | 120 | 1.9042 | 0.3375 | 0.4019 | 0.3375 | 0.2888 | | 1.8461 | 9.97 | 133 | 1.8521 | 0.3625 | 0.4132 | 0.3625 | 0.3021 | | 1.7917 | 10.95 | 146 | 1.8023 | 0.3688 | 0.4144 | 0.3688 | 0.3056 | | 1.7685 | 12.0 | 160 | 1.7552 | 0.375 | 0.4062 | 0.375 | 0.2978 | | 1.7072 | 12.97 | 173 | 1.7071 | 0.3875 | 0.4266 | 0.3875 | 0.3164 | | 1.6926 | 13.95 | 186 | 1.6742 | 0.375 | 0.4056 | 0.375 | 0.2996 | | 1.6084 | 15.0 | 200 | 1.6476 | 0.3937 | 0.4411 | 0.3937 | 0.3358 | | 1.6264 | 15.97 | 213 | 1.6231 | 0.3812 | 0.4357 | 0.3812 | 0.3311 | | 1.5531 | 16.95 | 226 | 1.6019 | 0.4125 | 0.4676 | 0.4125 | 0.3626 | | 1.5804 | 18.0 | 240 | 1.5773 | 0.3937 | 0.4442 | 0.3937 | 0.3428 | | 1.54 | 18.98 | 253 | 1.5606 | 0.4 | 0.4565 | 0.4 | 0.3527 | | 1.5461 | 19.95 | 266 | 1.5464 | 0.4437 | 0.5084 | 0.4437 | 0.4028 | | 1.4841 | 21.0 | 280 | 1.5323 | 0.4313 | 0.4950 | 0.4313 | 0.3881 | | 1.4765 | 21.98 | 293 | 1.5121 | 0.4313 | 0.4884 | 0.4313 | 0.3822 | | 1.4838 | 22.95 | 306 | 1.4978 | 0.4375 | 0.5138 | 0.4375 | 0.4012 | | 1.4487 | 24.0 | 320 | 1.4791 | 0.4437 | 0.5059 | 0.4437 | 0.4001 | | 1.4272 | 24.98 | 333 | 1.4617 | 0.4562 | 0.5304 | 0.4562 | 0.4180 | | 1.3886 | 25.95 | 346 | 1.4488 | 0.4625 | 0.5418 | 0.4625 | 0.4303 | | 1.4529 | 27.0 | 360 | 1.4436 | 0.45 | 0.5147 | 0.45 | 0.4035 | | 1.3894 | 27.98 | 373 | 1.4267 | 0.4688 | 0.5488 | 0.4688 | 0.4355 | | 1.3848 | 28.95 | 386 | 1.4153 | 0.4625 | 0.5337 | 0.4625 | 0.4264 | | 1.3561 | 30.0 | 400 | 1.3993 | 0.4875 | 0.5521 | 0.4875 | 0.4554 | | 1.3184 | 30.98 | 413 | 1.3852 | 0.4813 | 0.5526 | 0.4813 | 0.4470 | | 1.282 | 31.95 | 426 | 1.3703 | 0.4813 | 0.5480 | 0.4813 | 0.4449 | | 1.2988 | 33.0 | 440 | 1.3674 | 0.4688 | 0.5541 | 0.4688 | 0.4395 | | 1.2507 | 33.98 | 453 | 1.3594 | 0.4688 | 0.5347 | 0.4688 | 0.4307 | | 1.2446 | 34.95 | 466 | 1.3519 | 0.4813 | 0.5616 | 0.4813 | 0.4514 | | 1.2877 | 36.0 | 480 | 1.3547 | 0.4875 | 0.5599 | 0.4875 | 0.4605 | | 1.2237 | 36.98 | 493 | 1.3342 | 0.5 | 0.5744 | 0.5 | 0.4654 | | 1.2416 | 37.95 | 506 | 1.3214 | 0.4813 | 0.5693 | 0.4813 | 0.4551 | | 1.1786 | 39.0 | 520 | 1.3122 | 0.4875 | 0.5674 | 0.4875 | 0.4586 | | 1.193 | 39.98 | 533 | 1.2989 | 0.5 | 0.5755 | 0.5 | 0.4774 | | 1.148 | 40.95 | 546 | 1.2962 | 0.5125 | 0.5811 | 0.5125 | 0.4755 | | 1.1904 | 42.0 | 560 | 1.2860 | 0.5188 | 0.5863 | 0.5188 | 0.4928 | | 1.1311 | 42.98 | 573 | 1.2893 | 0.5312 | 0.5936 | 0.5312 | 0.5117 | | 1.1396 | 43.95 | 586 | 1.2860 | 0.4938 | 0.5633 | 0.4938 | 0.4698 | | 1.1235 | 45.0 | 600 | 1.2802 | 0.5 | 0.5725 | 0.5 | 0.4758 | | 1.1638 | 45.98 | 613 | 1.2596 | 0.525 | 0.5909 | 0.525 | 0.5058 | | 1.0777 | 46.95 | 626 | 1.2668 | 0.5188 | 0.5796 | 0.5188 | 0.4861 | | 1.1136 | 48.0 | 640 | 1.2520 | 0.55 | 0.6100 | 0.55 | 0.5291 | | 1.047 | 48.98 | 653 | 1.2437 | 0.5375 | 0.5963 | 0.5375 | 0.5279 | | 1.1101 | 49.95 | 666 | 1.2527 | 0.55 | 0.6195 | 0.55 | 0.5279 | | 1.0412 | 51.0 | 680 | 1.2455 | 0.525 | 0.5927 | 0.525 | 0.5156 | | 1.041 | 51.98 | 693 | 1.2245 | 0.55 | 0.6073 | 0.55 | 0.5353 | | 0.9906 | 52.95 | 706 | 1.2307 | 0.575 | 0.6420 | 0.575 | 0.5600 | | 0.9863 | 54.0 | 720 | 1.2307 | 0.5563 | 0.6150 | 0.5563 | 0.5362 | | 0.943 | 54.98 | 733 | 1.2270 | 0.55 | 0.6152 | 0.55 | 0.5302 | | 0.9557 | 55.95 | 746 | 1.2063 | 0.5312 | 0.5964 | 0.5312 | 0.5239 | | 0.9518 | 57.0 | 760 | 1.2122 | 0.55 | 0.6232 | 0.55 | 0.5433 | | 0.9545 | 57.98 | 773 | 1.1955 | 0.575 | 0.6144 | 0.575 | 0.5563 | | 0.9195 | 58.95 | 786 | 1.2139 | 0.5563 | 0.6052 | 0.5563 | 0.5459 | | 0.9267 | 60.0 | 800 | 1.1907 | 0.5687 | 0.6052 | 0.5687 | 0.5595 | | 0.9384 | 60.98 | 813 | 1.1899 | 0.575 | 0.6449 | 0.575 | 0.5650 | | 0.8727 | 61.95 | 826 | 1.1854 | 0.5813 | 0.6312 | 0.5813 | 0.5651 | | 0.8541 | 63.0 | 840 | 1.1957 | 0.575 | 0.6407 | 0.575 | 0.5632 | | 0.8899 | 63.98 | 853 | 1.1604 | 0.575 | 0.6196 | 0.575 | 0.5694 | | 0.9036 | 64.95 | 866 | 1.1859 | 0.5563 | 0.6310 | 0.5563 | 0.5306 | | 0.8177 | 66.0 | 880 | 1.1498 | 0.6125 | 0.6316 | 0.6125 | 0.6116 | | 0.7854 | 66.97 | 893 | 1.1842 | 0.5687 | 0.6142 | 0.5687 | 0.5582 | | 0.8054 | 67.95 | 906 | 1.1695 | 0.5938 | 0.6275 | 0.5938 | 0.5830 | | 0.8582 | 69.0 | 920 | 1.1882 | 0.5687 | 0.6057 | 0.5687 | 0.5495 | | 0.7603 | 69.97 | 933 | 1.2067 | 0.55 | 0.6025 | 0.55 | 0.5348 | | 0.763 | 70.95 | 946 | 1.1690 | 0.5625 | 0.6036 | 0.5625 | 0.5439 | | 0.8261 | 72.0 | 960 | 1.1616 | 0.6062 | 0.6306 | 0.6062 | 0.6016 | | 0.884 | 72.97 | 973 | 1.1952 | 0.5625 | 0.6082 | 0.5625 | 0.5436 | | 0.7843 | 73.95 | 986 | 1.1583 | 0.5687 | 0.5953 | 0.5687 | 0.5633 | | 0.801 | 75.0 | 1000 | 1.1547 | 0.575 | 0.6013 | 0.575 | 0.5745 | | 0.7454 | 75.97 | 1013 | 1.1372 | 0.5875 | 0.6193 | 0.5875 | 0.5761 | | 0.7325 | 76.95 | 1026 | 1.1696 | 0.5938 | 0.6351 | 0.5938 | 0.5919 | | 0.7931 | 78.0 | 1040 | 1.1511 | 0.6062 | 0.6342 | 0.6062 | 0.6053 | | 0.7487 | 78.97 | 1053 | 1.1655 | 0.5625 | 0.5898 | 0.5625 | 0.5496 | | 0.7262 | 79.95 | 1066 | 1.1394 | 0.6125 | 0.6295 | 0.6125 | 0.6048 | | 0.7669 | 81.0 | 1080 | 1.1748 | 0.575 | 0.5966 | 0.575 | 0.5697 | | 0.7028 | 81.97 | 1093 | 1.1418 | 0.5875 | 0.6178 | 0.5875 | 0.5885 | | 0.7749 | 82.95 | 1106 | 1.1736 | 0.55 | 0.5446 | 0.55 | 0.5255 | | 0.7233 | 84.0 | 1120 | 1.1645 | 0.5813 | 0.5973 | 0.5813 | 0.5699 | | 0.5915 | 84.97 | 1133 | 1.1376 | 0.5875 | 0.6167 | 0.5875 | 0.5867 | | 0.6985 | 85.95 | 1146 | 1.1665 | 0.5687 | 0.5868 | 0.5687 | 0.5533 | | 0.6572 | 87.0 | 1160 | 1.1341 | 0.6 | 0.6245 | 0.6 | 0.5963 | | 0.6317 | 87.97 | 1173 | 1.1327 | 0.6125 | 0.6288 | 0.6125 | 0.6026 | | 0.6546 | 88.95 | 1186 | 1.1668 | 0.5687 | 0.5797 | 0.5687 | 0.5528 | | 0.5801 | 90.0 | 1200 | 1.1521 | 0.5875 | 0.6161 | 0.5875 | 0.5818 | | 0.6958 | 90.97 | 1213 | 1.1401 | 0.5875 | 0.6083 | 0.5875 | 0.5774 | | 0.5856 | 91.95 | 1226 | 1.1379 | 0.5875 | 0.5888 | 0.5875 | 0.5760 | | 0.6281 | 93.0 | 1240 | 1.1379 | 0.6125 | 0.6429 | 0.6125 | 0.6123 | | 0.6518 | 93.97 | 1253 | 1.1619 | 0.6312 | 0.6547 | 0.6312 | 0.6247 | | 0.6055 | 94.95 | 1266 | 1.1700 | 0.575 | 0.5962 | 0.575 | 0.5673 | | 0.6181 | 96.0 | 1280 | 1.1550 | 0.5938 | 0.6281 | 0.5938 | 0.5970 | | 0.6601 | 96.97 | 1293 | 1.1334 | 0.6375 | 0.6498 | 0.6375 | 0.6341 | | 0.6112 | 97.5 | 1300 | 1.1007 | 0.6188 | 0.6341 | 0.6188 | 0.6207 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1