--- base_model: laszlokiss27/doodle-dash2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: results results: [] --- # results This model is a fine-tuned version of [laszlokiss27/doodle-dash2](https://huggingface.co/laszlokiss27/doodle-dash2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7177 - Accuracy: 0.8121 ## 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: 0.0008 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:------:|:---------------:|:--------:| | 0.9709 | 0.0256 | 5000 | 0.9170 | 0.7612 | | 0.9635 | 0.0513 | 10000 | 0.9147 | 0.7623 | | 0.9518 | 0.0769 | 15000 | 0.9081 | 0.7646 | | 0.9472 | 0.1026 | 20000 | 0.9044 | 0.7656 | | 0.9443 | 0.1282 | 25000 | 0.9061 | 0.7660 | | 0.93 | 0.1538 | 30000 | 0.9071 | 0.7651 | | 0.9206 | 0.1795 | 35000 | 0.8963 | 0.7680 | | 0.9214 | 0.2051 | 40000 | 0.8910 | 0.7693 | | 0.912 | 0.2308 | 45000 | 0.8914 | 0.7687 | | 0.9113 | 0.2564 | 50000 | 0.8801 | 0.7719 | | 0.9035 | 0.2820 | 55000 | 0.8803 | 0.7723 | | 0.9035 | 0.3077 | 60000 | 0.8798 | 0.7717 | | 0.8898 | 0.3333 | 65000 | 0.8822 | 0.7719 | | 0.8874 | 0.3590 | 70000 | 0.8703 | 0.7748 | | 0.8848 | 0.3846 | 75000 | 0.8623 | 0.7764 | | 0.8817 | 0.4102 | 80000 | 0.8609 | 0.7766 | | 0.8765 | 0.4359 | 85000 | 0.8599 | 0.7769 | | 0.8763 | 0.4615 | 90000 | 0.8532 | 0.7787 | | 0.8714 | 0.4872 | 95000 | 0.8572 | 0.7774 | | 0.869 | 0.5128 | 100000 | 0.8479 | 0.7796 | | 0.8672 | 0.5384 | 105000 | 0.8480 | 0.7798 | | 0.8632 | 0.5641 | 110000 | 0.8520 | 0.7792 | | 0.8592 | 0.5897 | 115000 | 0.8433 | 0.7811 | | 0.8607 | 0.6154 | 120000 | 0.8428 | 0.7811 | | 0.853 | 0.6410 | 125000 | 0.8375 | 0.7827 | | 0.8541 | 0.6666 | 130000 | 0.8455 | 0.7805 | | 0.8473 | 0.6923 | 135000 | 0.8330 | 0.7838 | | 0.8449 | 0.7179 | 140000 | 0.8305 | 0.7838 | | 0.8465 | 0.7436 | 145000 | 0.8274 | 0.7850 | | 0.8423 | 0.7692 | 150000 | 0.8325 | 0.7836 | | 0.8454 | 0.7948 | 155000 | 0.8270 | 0.7849 | | 0.8358 | 0.8205 | 160000 | 0.8328 | 0.7838 | | 0.8389 | 0.8461 | 165000 | 0.8209 | 0.7868 | | 0.8332 | 0.8718 | 170000 | 0.8340 | 0.7834 | | 0.8357 | 0.8974 | 175000 | 0.8200 | 0.7864 | | 0.8356 | 0.9230 | 180000 | 0.8162 | 0.7877 | | 0.835 | 0.9487 | 185000 | 0.8181 | 0.7874 | | 0.8298 | 0.9743 | 190000 | 0.8180 | 0.7874 | | 0.8285 | 1.0000 | 195000 | 0.8154 | 0.7878 | | 0.8138 | 1.0256 | 200000 | 0.8119 | 0.7889 | | 0.8104 | 1.0512 | 205000 | 0.8087 | 0.7887 | | 0.8162 | 1.0769 | 210000 | 0.8073 | 0.7895 | | 0.8122 | 1.1025 | 215000 | 0.8053 | 0.7902 | | 0.807 | 1.1282 | 220000 | 0.8064 | 0.7900 | | 0.8114 | 1.1538 | 225000 | 0.8043 | 0.7907 | | 0.8165 | 1.1794 | 230000 | 0.8042 | 0.7911 | | 0.8124 | 1.2051 | 235000 | 0.8009 | 0.7910 | | 0.8092 | 1.2307 | 240000 | 0.8019 | 0.7914 | | 0.8023 | 1.2564 | 245000 | 0.7979 | 0.7921 | | 0.8058 | 1.2820 | 250000 | 0.7988 | 0.7922 | | 0.8057 | 1.3076 | 255000 | 0.7976 | 0.7923 | | 0.8076 | 1.3333 | 260000 | 0.7976 | 0.7921 | | 0.805 | 1.3589 | 265000 | 0.7953 | 0.7930 | | 0.797 | 1.3846 | 270000 | 0.7990 | 0.7926 | | 0.7997 | 1.4102 | 275000 | 0.7929 | 0.7935 | | 0.8028 | 1.4358 | 280000 | 0.7933 | 0.7933 | | 0.7981 | 1.4615 | 285000 | 0.7905 | 0.7934 | | 0.8002 | 1.4871 | 290000 | 0.7965 | 0.7924 | | 0.7984 | 1.5128 | 295000 | 0.7915 | 0.7933 | | 0.7973 | 1.5384 | 300000 | 0.7950 | 0.7932 | | 0.7933 | 1.5640 | 305000 | 0.7865 | 0.7950 | | 0.7927 | 1.5897 | 310000 | 0.7886 | 0.7946 | | 0.799 | 1.6153 | 315000 | 0.7840 | 0.7954 | | 0.7961 | 1.6410 | 320000 | 0.8132 | 0.7901 | | 0.7866 | 1.6666 | 325000 | 0.7829 | 0.7958 | | 0.7898 | 1.6922 | 330000 | 0.7813 | 0.7959 | | 0.7885 | 1.7179 | 335000 | 0.7796 | 0.7969 | | 0.7901 | 1.7435 | 340000 | 0.7817 | 0.7958 | | 0.7916 | 1.7692 | 345000 | 0.7823 | 0.7962 | | 0.787 | 1.7948 | 350000 | 0.7789 | 0.7969 | | 0.7822 | 1.8204 | 355000 | 0.7787 | 0.7968 | | 0.7844 | 1.8461 | 360000 | 0.7754 | 0.7981 | | 0.7849 | 1.8717 | 365000 | 0.7775 | 0.7972 | | 0.7845 | 1.8974 | 370000 | 0.7761 | 0.7973 | | 0.7905 | 1.9230 | 375000 | 0.7736 | 0.7983 | | 0.788 | 1.9486 | 380000 | 0.7738 | 0.7978 | | 0.7832 | 1.9743 | 385000 | 0.7719 | 0.7980 | | 0.7787 | 1.9999 | 390000 | 0.7710 | 0.7986 | | 0.767 | 2.0256 | 395000 | 0.7717 | 0.7985 | | 0.7666 | 2.0512 | 400000 | 0.7698 | 0.7989 | | 0.7631 | 2.0768 | 405000 | 0.7719 | 0.7982 | | 0.7634 | 2.1025 | 410000 | 0.7684 | 0.7994 | | 0.7621 | 2.1281 | 415000 | 0.7707 | 0.7987 | | 0.7694 | 2.1538 | 420000 | 0.7700 | 0.7994 | | 0.7648 | 2.1794 | 425000 | 0.7678 | 0.7995 | | 0.7612 | 2.2050 | 430000 | 0.7673 | 0.7995 | | 0.7627 | 2.2307 | 435000 | 0.7671 | 0.7997 | | 0.766 | 2.2563 | 440000 | 0.7649 | 0.8003 | | 0.7635 | 2.2820 | 445000 | 0.7653 | 0.8000 | | 0.761 | 2.3076 | 450000 | 0.7647 | 0.8000 | | 0.7649 | 2.3332 | 455000 | 0.7661 | 0.8001 | | 0.7589 | 2.3589 | 460000 | 0.7630 | 0.8005 | | 0.7586 | 2.3845 | 465000 | 0.7703 | 0.7988 | | 0.7595 | 2.4102 | 470000 | 0.7640 | 0.8003 | | 0.7622 | 2.4358 | 475000 | 0.7627 | 0.8005 | | 0.7593 | 2.4614 | 480000 | 0.7605 | 0.8013 | | 0.7558 | 2.4871 | 485000 | 0.7609 | 0.8012 | | 0.7599 | 2.5127 | 490000 | 0.7651 | 0.8002 | | 0.7587 | 2.5384 | 495000 | 0.7589 | 0.8016 | | 0.7588 | 2.5640 | 500000 | 0.7570 | 0.8024 | | 0.762 | 2.5896 | 505000 | 0.7566 | 0.8020 | | 0.7526 | 2.6153 | 510000 | 0.7602 | 0.8013 | | 0.7587 | 2.6409 | 515000 | 0.7560 | 0.8021 | | 0.7522 | 2.6666 | 520000 | 0.7557 | 0.8026 | | 0.7546 | 2.6922 | 525000 | 0.7542 | 0.8026 | | 0.7542 | 2.7178 | 530000 | 0.7543 | 0.8029 | | 0.7509 | 2.7435 | 535000 | 0.7542 | 0.8029 | | 0.7515 | 2.7691 | 540000 | 0.7585 | 0.8016 | | 0.7508 | 2.7948 | 545000 | 0.7553 | 0.8024 | | 0.7523 | 2.8204 | 550000 | 0.7531 | 0.8028 | | 0.756 | 2.8460 | 555000 | 0.7511 | 0.8035 | | 0.7559 | 2.8717 | 560000 | 0.7500 | 0.8038 | | 0.75 | 2.8973 | 565000 | 0.7494 | 0.8038 | | 0.7492 | 2.9230 | 570000 | 0.7511 | 0.8035 | | 0.7481 | 2.9486 | 575000 | 0.7471 | 0.8044 | | 0.751 | 2.9742 | 580000 | 0.7478 | 0.8043 | | 0.7545 | 2.9999 | 585000 | 0.7595 | 0.8019 | | 0.7299 | 3.0255 | 590000 | 0.7478 | 0.8042 | | 0.7305 | 3.0512 | 595000 | 0.7487 | 0.8047 | | 0.7343 | 3.0768 | 600000 | 0.7466 | 0.8047 | | 0.731 | 3.1024 | 605000 | 0.7472 | 0.8045 | | 0.733 | 3.1281 | 610000 | 0.7460 | 0.8046 | | 0.7351 | 3.1537 | 615000 | 0.7486 | 0.8043 | | 0.7372 | 3.1794 | 620000 | 0.7446 | 0.8052 | | 0.7299 | 3.2050 | 625000 | 0.7478 | 0.8045 | | 0.7351 | 3.2306 | 630000 | 0.7458 | 0.8047 | | 0.7304 | 3.2563 | 635000 | 0.7460 | 0.8049 | | 0.7335 | 3.2819 | 640000 | 0.7451 | 0.8049 | | 0.7351 | 3.3076 | 645000 | 0.7416 | 0.8058 | | 0.7324 | 3.3332 | 650000 | 0.7420 | 0.8058 | | 0.732 | 3.3588 | 655000 | 0.7426 | 0.8057 | | 0.7286 | 3.3845 | 660000 | 0.7418 | 0.8062 | | 0.7331 | 3.4101 | 665000 | 0.7420 | 0.8059 | | 0.729 | 3.4358 | 670000 | 0.7402 | 0.8065 | | 0.7336 | 3.4614 | 675000 | 0.7409 | 0.8063 | | 0.7275 | 3.4870 | 680000 | 0.7398 | 0.8064 | | 0.7298 | 3.5127 | 685000 | 0.7388 | 0.8069 | | 0.724 | 3.5383 | 690000 | 0.7365 | 0.8070 | | 0.7266 | 3.5640 | 695000 | 0.7373 | 0.8072 | | 0.7282 | 3.5896 | 700000 | 0.7371 | 0.8074 | | 0.7272 | 3.6152 | 705000 | 0.7360 | 0.8073 | | 0.7227 | 3.6409 | 710000 | 0.7360 | 0.8072 | | 0.7275 | 3.6665 | 715000 | 0.7358 | 0.8073 | | 0.7299 | 3.6922 | 720000 | 0.7422 | 0.8063 | | 0.7363 | 3.7178 | 725000 | 0.7361 | 0.8072 | | 0.7274 | 3.7434 | 730000 | 0.7334 | 0.8082 | | 0.7282 | 3.7691 | 735000 | 0.7347 | 0.8081 | | 0.7239 | 3.7947 | 740000 | 0.7326 | 0.8085 | | 0.7225 | 3.8204 | 745000 | 0.7352 | 0.8076 | | 0.7242 | 3.8460 | 750000 | 0.7320 | 0.8086 | | 0.7291 | 3.8716 | 755000 | 0.7317 | 0.8089 | | 0.7292 | 3.8973 | 760000 | 0.7310 | 0.8087 | | 0.7247 | 3.9229 | 765000 | 0.7310 | 0.8083 | | 0.7286 | 3.9486 | 770000 | 0.7326 | 0.8084 | | 0.7237 | 3.9742 | 775000 | 0.7303 | 0.8088 | | 0.7187 | 3.9998 | 780000 | 0.7298 | 0.8090 | | 0.7077 | 4.0255 | 785000 | 0.7316 | 0.8084 | | 0.7108 | 4.0511 | 790000 | 0.7316 | 0.8084 | | 0.7025 | 4.0768 | 795000 | 0.7300 | 0.8093 | | 0.708 | 4.1024 | 800000 | 0.7295 | 0.8093 | | 0.7067 | 4.1280 | 805000 | 0.7288 | 0.8094 | | 0.7123 | 4.1537 | 810000 | 0.7287 | 0.8094 | | 0.707 | 4.1793 | 815000 | 0.7283 | 0.8095 | | 0.7033 | 4.2050 | 820000 | 0.7282 | 0.8099 | | 0.7128 | 4.2306 | 825000 | 0.7272 | 0.8099 | | 0.7053 | 4.2562 | 830000 | 0.7284 | 0.8095 | | 0.7097 | 4.2819 | 835000 | 0.7268 | 0.8098 | | 0.7101 | 4.3075 | 840000 | 0.7267 | 0.8097 | | 0.7074 | 4.3332 | 845000 | 0.7261 | 0.8102 | | 0.7034 | 4.3588 | 850000 | 0.7257 | 0.8101 | | 0.7059 | 4.3844 | 855000 | 0.7262 | 0.8098 | | 0.7008 | 4.4101 | 860000 | 0.7247 | 0.8100 | | 0.7021 | 4.4357 | 865000 | 0.7241 | 0.8103 | | 0.707 | 4.4614 | 870000 | 0.7243 | 0.8105 | | 0.7034 | 4.4870 | 875000 | 0.7238 | 0.8106 | | 0.7055 | 4.5126 | 880000 | 0.7233 | 0.8106 | | 0.7056 | 4.5383 | 885000 | 0.7231 | 0.8107 | | 0.7029 | 4.5639 | 890000 | 0.7226 | 0.8108 | | 0.7048 | 4.5896 | 895000 | 0.7224 | 0.8111 | | 0.7031 | 4.6152 | 900000 | 0.7221 | 0.8110 | | 0.7034 | 4.6408 | 905000 | 0.7216 | 0.8112 | | 0.7012 | 4.6665 | 910000 | 0.7218 | 0.8113 | | 0.702 | 4.6921 | 915000 | 0.7209 | 0.8114 | | 0.7018 | 4.7178 | 920000 | 0.7207 | 0.8115 | | 0.7056 | 4.7434 | 925000 | 0.7201 | 0.8116 | | 0.7005 | 4.7690 | 930000 | 0.7199 | 0.8118 | | 0.7005 | 4.7947 | 935000 | 0.7197 | 0.8117 | | 0.708 | 4.8203 | 940000 | 0.7189 | 0.8117 | | 0.6956 | 4.8460 | 945000 | 0.7190 | 0.8118 | | 0.7074 | 4.8716 | 950000 | 0.7185 | 0.8120 | | 0.6964 | 4.8972 | 955000 | 0.7184 | 0.8121 | | 0.7048 | 4.9229 | 960000 | 0.7188 | 0.8120 | | 0.7018 | 4.9485 | 965000 | 0.7178 | 0.8122 | | 0.7006 | 4.9742 | 970000 | 0.7177 | 0.8121 | | 0.7005 | 4.9998 | 975000 | 0.7177 | 0.8121 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1