generated_from_trainer

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videomae-base-ipm_all_videos_gb2

This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.4413 0.01 60 2.5408 0.0696
2.3949 1.01 120 2.5420 0.0435
2.5429 2.01 180 2.5626 0.0957
2.4678 3.01 240 2.5721 0.0783
2.3535 4.01 300 2.5703 0.0783
2.3525 5.01 360 2.5966 0.0609
2.2312 6.01 420 2.3565 0.1913
2.0797 7.01 480 2.0738 0.1826
2.1423 8.01 540 2.0182 0.2435
1.8594 9.01 600 2.9555 0.0957
2.2635 10.01 660 2.1157 0.1565
2.0527 11.01 720 1.7646 0.2870
1.4499 12.01 780 2.2083 0.2696
1.3273 13.01 840 2.4202 0.2609
1.4349 14.01 900 1.9185 0.3043
1.476 15.01 960 2.1430 0.2261
1.2768 16.01 1020 1.6487 0.3391
1.2488 17.01 1080 1.7203 0.3130
1.5273 18.01 1140 1.9167 0.2783
1.6865 19.01 1200 2.1734 0.2522
1.448 20.01 1260 2.2406 0.3043
1.3169 21.01 1320 1.8596 0.2261
1.3004 22.01 1380 2.1954 0.2957
1.2201 23.01 1440 1.8007 0.3391
1.7577 24.01 1500 2.2078 0.2696
1.3741 25.01 1560 1.8426 0.3217
1.3676 26.01 1620 1.8888 0.3826
1.5892 27.01 1680 2.0376 0.3043
1.1962 28.01 1740 1.7738 0.3130
1.4768 29.01 1800 1.3115 0.4522
1.4112 30.01 1860 1.4297 0.3739
1.2148 31.01 1920 1.9232 0.2870
1.1125 32.01 1980 1.8406 0.3217
0.9814 33.01 2040 2.0529 0.3913
1.0787 34.01 2100 1.5659 0.3391
1.4073 35.01 2160 1.7671 0.3478
1.2131 36.01 2220 1.5678 0.3130
1.1894 37.01 2280 1.5435 0.4087
1.2001 38.01 2340 1.6149 0.3913
1.518 39.01 2400 1.7457 0.2957
1.1231 40.01 2460 1.7148 0.4
0.9362 41.01 2520 1.5611 0.4174
1.1348 42.01 2580 1.2901 0.3826
0.9504 43.01 2640 1.4024 0.4
1.2008 44.01 2700 1.6685 0.4609
1.0468 45.01 2760 1.6202 0.4174
0.7304 46.01 2820 1.4007 0.4522
0.8522 47.01 2880 1.5439 0.4174
0.9106 48.01 2940 1.6536 0.4783
0.7837 49.01 3000 1.4113 0.4609
0.6869 50.01 3060 1.2071 0.5391
0.8787 51.01 3120 1.3023 0.5130
0.8072 52.01 3180 1.2058 0.6
0.9491 53.01 3240 1.5370 0.4957
0.7642 54.01 3300 1.2301 0.5652
0.6676 55.01 3360 1.4549 0.5391
0.8502 56.01 3420 1.6117 0.4522
1.0006 57.01 3480 1.3982 0.4957
0.8304 58.01 3540 1.3233 0.4783
0.9832 59.01 3600 1.2982 0.5478
0.3973 60.01 3660 1.3903 0.5478
0.9487 61.01 3720 1.4241 0.5304
0.9319 62.01 3780 1.4913 0.5565
0.6713 63.01 3840 1.4731 0.5826
0.7139 64.01 3900 1.0942 0.6870
0.7852 65.01 3960 1.2570 0.6348
1.0018 66.01 4020 1.1249 0.5913
0.7371 67.01 4080 1.4665 0.5565
0.6106 68.01 4140 1.7390 0.4957
0.8815 69.01 4200 1.5044 0.5652
0.6724 70.01 4260 1.8060 0.4957
0.5907 71.01 4320 1.5552 0.5391
0.6218 72.01 4380 1.6037 0.5826
0.7698 73.01 4440 1.4280 0.5913
0.6719 74.01 4500 1.6870 0.5565
0.3956 75.01 4560 1.6326 0.5217
0.6272 76.01 4620 1.3282 0.6
0.4354 77.01 4680 1.5181 0.5913
0.8649 78.01 4740 1.4137 0.5913
0.48 79.01 4800 1.6439 0.5913
0.9693 80.01 4860 1.6453 0.5739
0.3872 81.01 4920 1.5209 0.6696
0.913 82.01 4980 1.5002 0.6435
0.7185 83.01 5040 1.8319 0.5478
1.0149 84.01 5100 1.5270 0.5826
0.3811 85.01 5160 1.3813 0.6609
0.4902 86.01 5220 1.3160 0.6348
1.2717 87.01 5280 1.5052 0.6696
0.5379 88.01 5340 1.4357 0.6870
0.7101 89.01 5400 1.7699 0.5739
0.6517 90.01 5460 1.3428 0.6609
0.6213 91.01 5520 1.4725 0.6087
0.6995 92.01 5580 1.2645 0.6435
0.3997 93.01 5640 1.5827 0.5652
0.7778 94.01 5700 1.2344 0.7304
0.5093 95.01 5760 1.2908 0.6957
0.6022 96.01 5820 1.3528 0.6609
0.508 97.01 5880 1.4460 0.6783
0.4772 98.01 5940 1.1836 0.7478
0.8776 99.01 6000 1.4956 0.6435
0.7514 100.01 6060 1.4904 0.6609
0.1734 101.01 6120 1.6757 0.6087
0.5279 102.01 6180 1.8148 0.5913
0.2101 103.01 6240 1.4176 0.6348
0.6081 104.01 6300 1.7604 0.5913
0.2781 105.01 6360 1.7557 0.6087
0.2321 106.01 6420 1.3726 0.6696
0.4503 107.01 6480 1.6582 0.6348
0.4361 108.01 6540 2.0009 0.5913
0.4934 109.01 6600 1.9722 0.5217
0.3898 110.01 6660 1.5016 0.6696
0.4286 111.01 6720 1.5307 0.6783
0.2792 112.01 6780 1.5770 0.6696
0.2254 113.01 6840 1.7076 0.6522
0.1739 114.01 6900 2.0225 0.5826
0.1951 115.01 6960 1.8448 0.6174
0.614 116.01 7020 1.5507 0.6696
0.6894 117.01 7080 1.5430 0.6609
0.9059 118.01 7140 1.6563 0.6696
0.4592 119.01 7200 1.5566 0.7043
0.3895 120.01 7260 1.5251 0.7130
0.4897 121.01 7320 1.7417 0.6696
0.5362 122.01 7380 1.5845 0.6783
0.4484 123.01 7440 1.6405 0.6870
0.557 124.01 7500 1.5133 0.7130
0.4878 125.01 7560 1.3845 0.7391
0.2704 126.01 7620 1.4704 0.6957
0.7636 127.01 7680 1.4413 0.6957
0.4196 128.01 7740 1.4106 0.7043
0.5835 129.01 7800 1.2571 0.7391
0.6156 130.01 7860 1.8000 0.6609
0.3074 131.01 7920 1.7324 0.6435
0.4697 132.01 7980 1.5218 0.7043
0.2968 133.01 8040 1.3640 0.7391
0.452 134.01 8100 1.4916 0.7217
0.2699 135.01 8160 1.6554 0.6957
0.3889 136.01 8220 1.5015 0.7391
0.5006 137.01 8280 1.4134 0.7391
0.135 138.01 8340 1.3987 0.7565
0.3882 139.01 8400 1.4364 0.7304
0.194 140.01 8460 1.6716 0.6957
0.1185 141.01 8520 1.8543 0.6609
0.4103 142.01 8580 1.9628 0.6348
0.1577 143.01 8640 1.7975 0.6609
0.2213 144.01 8700 1.6324 0.6870
0.6129 145.01 8760 1.5654 0.7130
0.54 146.01 8820 1.4210 0.7565
0.357 147.01 8880 1.4255 0.7478
0.2451 148.01 8940 1.6774 0.6957
0.4752 149.01 9000 1.7326 0.6957
0.1847 150.01 9060 1.7124 0.6609
0.2618 151.01 9120 1.6317 0.6783
0.4884 152.01 9180 1.6136 0.6870
0.4929 153.01 9240 1.5062 0.7217
0.5781 154.01 9300 1.4666 0.7217
0.4633 155.01 9360 1.5033 0.7043
0.5355 156.01 9420 1.4821 0.6957
0.551 157.01 9480 1.4866 0.6957
0.3247 158.01 9540 1.5070 0.6957
0.5455 159.01 9600 1.5087 0.6957

Framework versions