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mobilevitv2
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laszlokiss27
doodle-dash-vit2
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metadata
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 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