--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - image_folder metrics: - accuracy - precision - f1 model-index: - name: emotion_classification results: - task: name: Image Classification type: image-classification dataset: name: image_folder type: image_folder config: FastJobs--Visual_Emotional_Analysis split: train args: FastJobs--Visual_Emotional_Analysis metrics: - name: Accuracy type: accuracy value: 0.6625 - name: Precision type: precision value: 0.6857332900074835 - name: F1 type: f1 value: 0.6658368805611075 --- # emotion_classification 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 image_folder dataset. It achieves the following results on the evaluation set: - Loss: 1.1720 - Accuracy: 0.6625 - Precision: 0.6857 - F1: 0.6658 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 150 - num_epochs: 300 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:| | 2.0805 | 1.0 | 10 | 2.0844 | 0.1688 | 0.1731 | 0.1670 | | 2.0876 | 2.0 | 20 | 2.0807 | 0.1938 | 0.1814 | 0.1843 | | 2.0786 | 3.0 | 30 | 2.0741 | 0.1812 | 0.1658 | 0.1702 | | 2.0653 | 4.0 | 40 | 2.0663 | 0.2062 | 0.1832 | 0.1893 | | 2.0586 | 5.0 | 50 | 2.0547 | 0.2062 | 0.1817 | 0.1911 | | 2.0347 | 6.0 | 60 | 2.0343 | 0.2375 | 0.2074 | 0.2187 | | 2.009 | 7.0 | 70 | 2.0039 | 0.2875 | 0.4007 | 0.2750 | | 1.9672 | 8.0 | 80 | 1.9560 | 0.3187 | 0.3615 | 0.3006 | | 1.9015 | 9.0 | 90 | 1.8650 | 0.3688 | 0.4229 | 0.3577 | | 1.812 | 10.0 | 100 | 1.7339 | 0.4375 | 0.3925 | 0.4045 | | 1.6993 | 11.0 | 110 | 1.6196 | 0.4688 | 0.4093 | 0.4267 | | 1.6037 | 12.0 | 120 | 1.5466 | 0.475 | 0.4808 | 0.4413 | | 1.5332 | 13.0 | 130 | 1.4791 | 0.525 | 0.4749 | 0.4922 | | 1.4649 | 14.0 | 140 | 1.4201 | 0.525 | 0.4860 | 0.4948 | | 1.4142 | 15.0 | 150 | 1.3659 | 0.55 | 0.5231 | 0.5178 | | 1.3826 | 16.0 | 160 | 1.3001 | 0.575 | 0.5346 | 0.5434 | | 1.3048 | 17.0 | 170 | 1.2689 | 0.5813 | 0.5381 | 0.5535 | | 1.2519 | 18.0 | 180 | 1.2334 | 0.575 | 0.5816 | 0.5580 | | 1.2043 | 19.0 | 190 | 1.2186 | 0.55 | 0.5739 | 0.5424 | | 1.1575 | 20.0 | 200 | 1.1711 | 0.5687 | 0.5421 | 0.5371 | | 1.0957 | 21.0 | 210 | 1.1674 | 0.5938 | 0.5764 | 0.5645 | | 1.0719 | 22.0 | 220 | 1.1473 | 0.5875 | 0.5899 | 0.5721 | | 0.9894 | 23.0 | 230 | 1.1303 | 0.6125 | 0.6507 | 0.6124 | | 0.9698 | 24.0 | 240 | 1.1010 | 0.6188 | 0.6323 | 0.6142 | | 0.9081 | 25.0 | 250 | 1.1038 | 0.5938 | 0.6074 | 0.5923 | | 0.8739 | 26.0 | 260 | 1.1383 | 0.5563 | 0.5874 | 0.5447 | | 0.8815 | 27.0 | 270 | 1.1483 | 0.6 | 0.6524 | 0.5894 | | 0.8426 | 28.0 | 280 | 1.1212 | 0.5813 | 0.6356 | 0.5703 | | 0.7614 | 29.0 | 290 | 1.1002 | 0.6188 | 0.6724 | 0.6089 | | 0.7937 | 30.0 | 300 | 1.0272 | 0.6188 | 0.6515 | 0.6135 | | 0.7379 | 31.0 | 310 | 1.0184 | 0.6062 | 0.6120 | 0.6035 | | 0.6994 | 32.0 | 320 | 1.0163 | 0.5875 | 0.5966 | 0.5772 | | 0.684 | 33.0 | 330 | 1.0420 | 0.6312 | 0.6627 | 0.6327 | | 0.605 | 34.0 | 340 | 1.0555 | 0.6312 | 0.6822 | 0.6353 | | 0.5851 | 35.0 | 350 | 1.0991 | 0.625 | 0.6941 | 0.6269 | | 0.6186 | 36.0 | 360 | 1.1196 | 0.6188 | 0.6916 | 0.6077 | | 0.5349 | 37.0 | 370 | 1.0707 | 0.6062 | 0.6123 | 0.5978 | | 0.5549 | 38.0 | 380 | 1.0161 | 0.6375 | 0.6498 | 0.6308 | | 0.577 | 39.0 | 390 | 1.1375 | 0.5813 | 0.6449 | 0.5770 | | 0.5151 | 40.0 | 400 | 1.0479 | 0.65 | 0.6691 | 0.6421 | | 0.4898 | 41.0 | 410 | 1.0835 | 0.6125 | 0.6378 | 0.6106 | | 0.4619 | 42.0 | 420 | 1.0262 | 0.6375 | 0.6596 | 0.6418 | | 0.4142 | 43.0 | 430 | 1.1238 | 0.6188 | 0.6422 | 0.6143 | | 0.4695 | 44.0 | 440 | 1.0765 | 0.65 | 0.6664 | 0.6424 | | 0.4195 | 45.0 | 450 | 1.0646 | 0.6375 | 0.6622 | 0.6357 | | 0.4144 | 46.0 | 460 | 1.1255 | 0.6 | 0.6308 | 0.6023 | | 0.3552 | 47.0 | 470 | 1.0580 | 0.6562 | 0.6639 | 0.6574 | | 0.3887 | 48.0 | 480 | 1.0673 | 0.6438 | 0.6560 | 0.6421 | | 0.348 | 49.0 | 490 | 1.1828 | 0.6062 | 0.6503 | 0.6041 | | 0.3284 | 50.0 | 500 | 1.1613 | 0.5625 | 0.5756 | 0.5585 | | 0.4082 | 51.0 | 510 | 1.1582 | 0.6188 | 0.6458 | 0.6154 | | 0.3929 | 52.0 | 520 | 1.1444 | 0.6188 | 0.6438 | 0.6117 | | 0.337 | 53.0 | 530 | 1.1073 | 0.6375 | 0.6497 | 0.6348 | | 0.3525 | 54.0 | 540 | 1.1750 | 0.6062 | 0.6331 | 0.6079 | | 0.3336 | 55.0 | 550 | 1.1841 | 0.6188 | 0.6435 | 0.6116 | | 0.2946 | 56.0 | 560 | 1.2258 | 0.5875 | 0.6250 | 0.5820 | | 0.332 | 57.0 | 570 | 1.1952 | 0.5938 | 0.6526 | 0.6018 | | 0.3013 | 58.0 | 580 | 1.1858 | 0.6438 | 0.6671 | 0.6465 | | 0.3035 | 59.0 | 590 | 1.1823 | 0.625 | 0.6326 | 0.6238 | | 0.3071 | 60.0 | 600 | 1.1567 | 0.6062 | 0.6348 | 0.6035 | | 0.2783 | 61.0 | 610 | 1.1536 | 0.6188 | 0.6360 | 0.6178 | | 0.2901 | 62.0 | 620 | 1.1183 | 0.6312 | 0.6412 | 0.6300 | | 0.3046 | 63.0 | 630 | 1.1705 | 0.6 | 0.6209 | 0.6026 | | 0.3066 | 64.0 | 640 | 1.1717 | 0.6375 | 0.6501 | 0.6328 | | 0.2978 | 65.0 | 650 | 1.1669 | 0.6375 | 0.6539 | 0.6332 | | 0.2967 | 66.0 | 660 | 1.2839 | 0.6188 | 0.6552 | 0.6097 | | 0.3624 | 67.0 | 670 | 1.2095 | 0.625 | 0.6622 | 0.6170 | | 0.2683 | 68.0 | 680 | 1.2292 | 0.6125 | 0.6504 | 0.6159 | | 0.2862 | 69.0 | 690 | 1.2228 | 0.6125 | 0.6252 | 0.6061 | | 0.252 | 70.0 | 700 | 1.4087 | 0.575 | 0.6327 | 0.5738 | | 0.2968 | 71.0 | 710 | 1.1559 | 0.6562 | 0.6769 | 0.6585 | | 0.247 | 72.0 | 720 | 1.1829 | 0.6062 | 0.6333 | 0.6108 | | 0.2849 | 73.0 | 730 | 1.2207 | 0.6312 | 0.6863 | 0.6321 | | 0.2684 | 74.0 | 740 | 1.1720 | 0.6625 | 0.6857 | 0.6658 | | 0.2649 | 75.0 | 750 | 1.2352 | 0.6375 | 0.6479 | 0.6359 | | 0.2265 | 76.0 | 760 | 1.2990 | 0.6 | 0.6427 | 0.6002 | | 0.2398 | 77.0 | 770 | 1.3163 | 0.6 | 0.6420 | 0.6007 | | 0.2324 | 78.0 | 780 | 1.3362 | 0.5938 | 0.5907 | 0.5730 | | 0.1927 | 79.0 | 790 | 1.2690 | 0.625 | 0.6552 | 0.6227 | | 0.1757 | 80.0 | 800 | 1.2791 | 0.65 | 0.6716 | 0.6487 | | 0.1993 | 81.0 | 810 | 1.2946 | 0.625 | 0.6564 | 0.6235 | | 0.2326 | 82.0 | 820 | 1.3964 | 0.5813 | 0.6042 | 0.5742 | | 0.2252 | 83.0 | 830 | 1.3020 | 0.6125 | 0.6567 | 0.6095 | | 0.228 | 84.0 | 840 | 1.2979 | 0.6312 | 0.6629 | 0.6358 | | 0.2055 | 85.0 | 850 | 1.2876 | 0.6125 | 0.6274 | 0.6086 | | 0.2171 | 86.0 | 860 | 1.2951 | 0.6312 | 0.6574 | 0.6308 | | 0.2156 | 87.0 | 870 | 1.3025 | 0.6 | 0.6072 | 0.5975 | | 0.1869 | 88.0 | 880 | 1.2232 | 0.6375 | 0.6822 | 0.6423 | | 0.2199 | 89.0 | 890 | 1.2538 | 0.6125 | 0.6056 | 0.6009 | | 0.189 | 90.0 | 900 | 1.3159 | 0.6188 | 0.6345 | 0.6198 | | 0.2023 | 91.0 | 910 | 1.3270 | 0.5938 | 0.6124 | 0.5910 | | 0.2304 | 92.0 | 920 | 1.2732 | 0.65 | 0.6642 | 0.6436 | | 0.2042 | 93.0 | 930 | 1.4199 | 0.55 | 0.5662 | 0.5401 | | 0.1968 | 94.0 | 940 | 1.4262 | 0.5875 | 0.6388 | 0.5828 | | 0.1968 | 95.0 | 950 | 1.3575 | 0.6062 | 0.6364 | 0.6090 | | 0.2176 | 96.0 | 960 | 1.3166 | 0.6062 | 0.6375 | 0.6080 | | 0.1884 | 97.0 | 970 | 1.2959 | 0.5875 | 0.6066 | 0.5876 | | 0.1841 | 98.0 | 980 | 1.4839 | 0.5875 | 0.6712 | 0.5838 | | 0.2175 | 99.0 | 990 | 1.3247 | 0.6125 | 0.6385 | 0.6086 | | 0.2091 | 100.0 | 1000 | 1.3601 | 0.6188 | 0.6490 | 0.6138 | | 0.1656 | 101.0 | 1010 | 1.4244 | 0.6062 | 0.6495 | 0.6077 | | 0.1897 | 102.0 | 1020 | 1.3256 | 0.6188 | 0.6774 | 0.6237 | | 0.1816 | 103.0 | 1030 | 1.3440 | 0.6062 | 0.6390 | 0.6097 | | 0.1973 | 104.0 | 1040 | 1.3377 | 0.625 | 0.6645 | 0.6240 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3