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videomae-base-finetuned-scratch_1

Video Classification
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videomae
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End of training

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  1. README.md +75 -39
  2. model.safetensors +1 -1
README.md CHANGED
@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8522
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- - Accuracy: 0.8209
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  ## Model description
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@@ -44,48 +44,84 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - training_steps: 11856
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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- | 0.6811 | 0.03 | 330 | 0.6314 | 0.6547 |
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- | 0.6706 | 1.03 | 660 | 0.6354 | 0.6318 |
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- | 0.6581 | 2.03 | 990 | 0.6328 | 0.6375 |
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- | 0.5614 | 3.03 | 1320 | 0.5507 | 0.7135 |
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- | 0.438 | 4.03 | 1650 | 0.5440 | 0.7264 |
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- | 0.4569 | 5.03 | 1980 | 0.5532 | 0.7278 |
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- | 0.3614 | 6.03 | 2310 | 0.5283 | 0.7364 |
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- | 0.3514 | 7.03 | 2640 | 0.6700 | 0.7407 |
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- | 0.2796 | 8.03 | 2970 | 0.7974 | 0.7178 |
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- | 0.236 | 9.03 | 3300 | 0.6851 | 0.7722 |
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- | 0.2066 | 10.03 | 3630 | 0.7625 | 0.7708 |
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- | 0.2831 | 11.03 | 3960 | 0.7963 | 0.7708 |
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- | 0.1903 | 12.03 | 4290 | 1.0343 | 0.7722 |
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- | 0.1169 | 13.03 | 4620 | 0.8528 | 0.7865 |
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- | 0.3502 | 14.03 | 4950 | 0.9265 | 0.7966 |
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- | 0.1728 | 15.03 | 5280 | 0.8522 | 0.8209 |
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- | 0.0542 | 16.03 | 5610 | 1.0107 | 0.8052 |
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- | 0.0711 | 17.03 | 5940 | 0.9795 | 0.8080 |
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- | 0.0287 | 18.03 | 6270 | 1.1471 | 0.8095 |
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- | 0.1011 | 19.03 | 6600 | 1.0894 | 0.7851 |
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- | 0.0424 | 20.03 | 6930 | 1.1444 | 0.7822 |
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- | 0.0229 | 21.03 | 7260 | 1.3766 | 0.7822 |
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- | 0.058 | 22.03 | 7590 | 1.2796 | 0.7894 |
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- | 0.1045 | 23.03 | 7920 | 1.3585 | 0.7751 |
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- | 0.0379 | 24.03 | 8250 | 1.3156 | 0.7837 |
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- | 0.0945 | 25.03 | 8580 | 1.3047 | 0.7923 |
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- | 0.0528 | 26.03 | 8910 | 1.3670 | 0.7894 |
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- | 0.0002 | 27.03 | 9240 | 1.3398 | 0.7966 |
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- | 0.0562 | 28.03 | 9570 | 1.3444 | 0.7980 |
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- | 0.0002 | 29.03 | 9900 | 1.2679 | 0.8166 |
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- | 0.0018 | 30.03 | 10230 | 1.3748 | 0.7966 |
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- | 0.0371 | 31.03 | 10560 | 1.4094 | 0.8080 |
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- | 0.0157 | 32.03 | 10890 | 1.4391 | 0.8023 |
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- | 0.0001 | 33.03 | 11220 | 1.3831 | 0.8052 |
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- | 0.0001 | 34.03 | 11550 | 1.3971 | 0.8080 |
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- | 0.0041 | 35.03 | 11856 | 1.3906 | 0.8080 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.9317
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+ - Accuracy: 0.75
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - training_steps: 30192
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 0.5872 | 0.01 | 420 | 0.6884 | 0.5518 |
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+ | 0.5358 | 1.01 | 840 | 0.6766 | 0.6194 |
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+ | 0.5339 | 2.01 | 1260 | 0.6295 | 0.6430 |
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+ | 0.4483 | 3.01 | 1680 | 0.7323 | 0.5957 |
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+ | 0.4654 | 4.01 | 2100 | 0.7020 | 0.6486 |
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+ | 0.3897 | 5.01 | 2520 | 0.7636 | 0.6498 |
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+ | 0.3386 | 6.01 | 2940 | 0.8877 | 0.6610 |
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+ | 0.3601 | 7.01 | 3360 | 0.8791 | 0.6486 |
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+ | 0.3401 | 8.01 | 3780 | 0.7403 | 0.6633 |
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+ | 0.3113 | 9.01 | 4200 | 0.7316 | 0.6959 |
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+ | 0.2096 | 10.01 | 4620 | 0.9519 | 0.6982 |
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+ | 0.1537 | 11.01 | 5040 | 0.9116 | 0.7016 |
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+ | 0.1113 | 12.01 | 5460 | 1.0047 | 0.6971 |
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+ | 0.3247 | 13.01 | 5880 | 1.2167 | 0.6847 |
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+ | 0.171 | 14.01 | 6300 | 0.9337 | 0.7027 |
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+ | 0.3076 | 15.01 | 6720 | 1.1811 | 0.7207 |
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+ | 0.2927 | 16.01 | 7140 | 1.0953 | 0.7218 |
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+ | 0.1679 | 17.01 | 7560 | 1.2948 | 0.7207 |
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+ | 0.1523 | 18.01 | 7980 | 1.3632 | 0.7016 |
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+ | 0.1059 | 19.01 | 8400 | 1.2915 | 0.7185 |
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+ | 0.1741 | 20.01 | 8820 | 1.2315 | 0.7432 |
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+ | 0.0629 | 21.01 | 9240 | 1.3948 | 0.7230 |
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+ | 0.0075 | 22.01 | 9660 | 1.1435 | 0.7376 |
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+ | 0.1692 | 23.01 | 10080 | 1.3998 | 0.7128 |
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+ | 0.0347 | 24.01 | 10500 | 1.4803 | 0.7027 |
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+ | 0.0396 | 25.01 | 10920 | 1.6457 | 0.7005 |
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+ | 0.0074 | 26.01 | 11340 | 1.5602 | 0.7050 |
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+ | 0.1256 | 27.01 | 11760 | 1.3965 | 0.7173 |
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+ | 0.0021 | 28.01 | 12180 | 1.4514 | 0.7342 |
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+ | 0.0476 | 29.01 | 12600 | 1.2915 | 0.7173 |
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+ | 0.0065 | 30.01 | 13020 | 1.3397 | 0.7095 |
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+ | 0.0435 | 31.01 | 13440 | 1.8912 | 0.6948 |
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+ | 0.0268 | 32.01 | 13860 | 1.5767 | 0.7286 |
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+ | 0.0487 | 33.01 | 14280 | 1.6439 | 0.6948 |
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+ | 0.0448 | 34.01 | 14700 | 1.5990 | 0.7354 |
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+ | 0.0166 | 35.01 | 15120 | 1.3866 | 0.7466 |
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+ | 0.1029 | 36.01 | 15540 | 1.7427 | 0.7106 |
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+ | 0.0678 | 37.01 | 15960 | 1.4194 | 0.7365 |
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+ | 0.0007 | 38.01 | 16380 | 1.9137 | 0.7072 |
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+ | 0.0602 | 39.01 | 16800 | 1.6180 | 0.7309 |
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+ | 0.0977 | 40.01 | 17220 | 1.5710 | 0.7354 |
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+ | 0.0606 | 41.01 | 17640 | 1.3908 | 0.7342 |
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+ | 0.1046 | 42.01 | 18060 | 1.7846 | 0.7252 |
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+ | 0.0004 | 43.01 | 18480 | 1.6396 | 0.7241 |
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+ | 0.0881 | 44.01 | 18900 | 1.6206 | 0.7196 |
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+ | 0.0934 | 45.01 | 19320 | 1.6994 | 0.7320 |
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+ | 0.0001 | 46.01 | 19740 | 2.0068 | 0.7162 |
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+ | 0.024 | 47.01 | 20160 | 1.5350 | 0.7376 |
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+ | 0.017 | 48.01 | 20580 | 1.8864 | 0.7162 |
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+ | 0.121 | 49.01 | 21000 | 1.6862 | 0.7230 |
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+ | 0.0001 | 50.01 | 21420 | 1.8462 | 0.7365 |
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+ | 0.0319 | 51.01 | 21840 | 1.9072 | 0.7286 |
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+ | 0.065 | 52.01 | 22260 | 1.6631 | 0.7556 |
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+ | 0.0424 | 53.01 | 22680 | 1.9177 | 0.7399 |
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+ | 0.0001 | 54.01 | 23100 | 1.8990 | 0.7365 |
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+ | 0.0 | 55.01 | 23520 | 2.0622 | 0.7455 |
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+ | 0.0582 | 56.01 | 23940 | 1.4821 | 0.7444 |
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+ | 0.0001 | 57.01 | 24360 | 1.6254 | 0.7624 |
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+ | 0.0 | 58.01 | 24780 | 1.8024 | 0.7545 |
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+ | 0.0486 | 59.01 | 25200 | 1.6804 | 0.7545 |
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+ | 0.0001 | 60.01 | 25620 | 1.7991 | 0.7523 |
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+ | 0.0 | 61.01 | 26040 | 1.8281 | 0.7511 |
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+ | 0.0022 | 62.01 | 26460 | 1.8172 | 0.75 |
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+ | 0.0001 | 63.01 | 26880 | 1.9532 | 0.7489 |
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+ | 0.0 | 64.01 | 27300 | 1.9209 | 0.7477 |
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+ | 0.0 | 65.01 | 27720 | 1.9100 | 0.7579 |
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+ | 0.0 | 66.01 | 28140 | 1.9572 | 0.7534 |
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+ | 0.0007 | 67.01 | 28560 | 2.0380 | 0.75 |
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+ | 0.0627 | 68.01 | 28980 | 1.8911 | 0.7579 |
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+ | 0.0002 | 69.01 | 29400 | 1.9255 | 0.75 |
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+ | 0.0 | 70.01 | 29820 | 1.9195 | 0.7568 |
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+ | 0.0 | 71.01 | 30192 | 1.9317 | 0.75 |
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  ### Framework versions
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