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Joy28
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videomae-base-finetuned-subset

Video Classification
Transformers
TensorBoard
Safetensors
videomae
Generated from Trainer
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xet
 Metrics Community
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  1. README.md +105 -65
  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: 0.8930
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- - Accuracy: 0.6296
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  ## Model description
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@@ -44,72 +44,112 @@ 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: 6660
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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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- | 1.6074 | 0.02 | 112 | 1.5690 | 0.3687 |
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- | 1.6001 | 1.02 | 224 | 1.5783 | 0.3041 |
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- | 1.4193 | 2.02 | 336 | 1.4874 | 0.3825 |
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- | 1.398 | 3.02 | 448 | 1.0197 | 0.6406 |
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- | 1.2217 | 4.02 | 560 | 1.3386 | 0.3917 |
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- | 1.2577 | 5.02 | 672 | 1.2196 | 0.5392 |
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- | 1.0121 | 6.02 | 784 | 1.2319 | 0.4793 |
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- | 1.2485 | 7.02 | 896 | 0.8230 | 0.7512 |
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- | 1.025 | 8.02 | 1008 | 0.8023 | 0.6866 |
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- | 1.2952 | 9.02 | 1120 | 0.9130 | 0.6037 |
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- | 0.9499 | 10.02 | 1232 | 1.0621 | 0.6037 |
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- | 0.8805 | 11.02 | 1344 | 0.8713 | 0.7051 |
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- | 1.2066 | 12.02 | 1456 | 0.9364 | 0.5853 |
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- | 0.9358 | 13.02 | 1568 | 0.9107 | 0.5853 |
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- | 0.9043 | 14.02 | 1680 | 0.9147 | 0.6359 |
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- | 0.8383 | 15.02 | 1792 | 0.9451 | 0.6359 |
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- | 0.7482 | 16.02 | 1904 | 0.8765 | 0.6221 |
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- | 0.9547 | 17.02 | 2016 | 0.7998 | 0.7650 |
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- | 0.7028 | 18.02 | 2128 | 0.9257 | 0.6406 |
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- | 0.8659 | 19.02 | 2240 | 1.0655 | 0.5853 |
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- | 0.5591 | 20.02 | 2352 | 1.2794 | 0.5760 |
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- | 0.8963 | 21.02 | 2464 | 1.0049 | 0.6959 |
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- | 0.9221 | 22.02 | 2576 | 1.1113 | 0.6083 |
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- | 0.7154 | 23.02 | 2688 | 0.9371 | 0.6406 |
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- | 0.8795 | 24.02 | 2800 | 0.6838 | 0.7235 |
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- | 0.631 | 25.02 | 2912 | 1.2093 | 0.6129 |
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- | 1.0489 | 26.02 | 3024 | 1.4720 | 0.5484 |
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- | 0.5881 | 27.02 | 3136 | 1.1905 | 0.6313 |
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- | 0.7919 | 28.02 | 3248 | 1.1292 | 0.5760 |
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- | 0.9158 | 29.02 | 3360 | 1.0214 | 0.6359 |
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- | 0.8319 | 30.02 | 3472 | 1.2862 | 0.6682 |
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- | 0.6775 | 31.02 | 3584 | 1.0971 | 0.6406 |
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- | 0.7191 | 32.02 | 3696 | 1.0264 | 0.6498 |
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- | 0.7662 | 33.02 | 3808 | 1.0589 | 0.6406 |
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- | 0.7313 | 34.02 | 3920 | 1.5076 | 0.5622 |
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- | 0.7539 | 35.02 | 4032 | 1.2265 | 0.5899 |
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- | 0.571 | 36.02 | 4144 | 1.1598 | 0.6267 |
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- | 0.3404 | 37.02 | 4256 | 1.0307 | 0.6359 |
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- | 0.5553 | 38.02 | 4368 | 0.8180 | 0.7235 |
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- | 0.8499 | 39.02 | 4480 | 1.0074 | 0.6498 |
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- | 0.5036 | 40.02 | 4592 | 1.1160 | 0.6313 |
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- | 0.814 | 41.02 | 4704 | 0.9032 | 0.6959 |
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- | 0.7293 | 42.02 | 4816 | 0.9331 | 0.7281 |
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- | 0.4402 | 43.02 | 4928 | 1.4190 | 0.5668 |
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- | 0.4625 | 44.02 | 5040 | 1.0268 | 0.7005 |
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- | 0.2266 | 45.02 | 5152 | 1.2808 | 0.6406 |
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- | 0.7424 | 46.02 | 5264 | 1.1821 | 0.6498 |
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- | 0.4852 | 47.02 | 5376 | 1.2434 | 0.6590 |
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- | 0.523 | 48.02 | 5488 | 1.2123 | 0.6267 |
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- | 0.8344 | 49.02 | 5600 | 1.1889 | 0.6636 |
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- | 0.6648 | 50.02 | 5712 | 1.2328 | 0.6406 |
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- | 0.6929 | 51.02 | 5824 | 1.3269 | 0.6129 |
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- | 0.4253 | 52.02 | 5936 | 1.1885 | 0.6820 |
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- | 0.7003 | 53.02 | 6048 | 1.1522 | 0.7005 |
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- | 0.4105 | 54.02 | 6160 | 1.0037 | 0.7373 |
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- | 0.5206 | 55.02 | 6272 | 1.0913 | 0.7189 |
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- | 0.7129 | 56.02 | 6384 | 1.1083 | 0.6866 |
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- | 0.4772 | 57.02 | 6496 | 1.1276 | 0.7143 |
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- | 0.4822 | 58.02 | 6608 | 1.0920 | 0.7235 |
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- | 0.6307 | 59.01 | 6660 | 1.0987 | 0.7189 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.7700
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+ - Accuracy: 0.6713
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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: 11100
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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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+ | 1.638 | 0.01 | 112 | 1.5736 | 0.1567 |
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+ | 1.5845 | 1.01 | 224 | 1.5841 | 0.2719 |
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+ | 1.4522 | 2.01 | 336 | 1.6293 | 0.2350 |
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+ | 1.3111 | 3.01 | 448 | 1.0450 | 0.6037 |
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+ | 1.2849 | 4.01 | 560 | 1.3186 | 0.4608 |
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+ | 1.3246 | 5.01 | 672 | 1.1759 | 0.5161 |
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+ | 1.3801 | 6.01 | 784 | 1.2188 | 0.4608 |
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+ | 1.3228 | 7.01 | 896 | 0.9895 | 0.6406 |
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+ | 0.9706 | 8.01 | 1008 | 1.1265 | 0.6129 |
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+ | 1.2483 | 9.01 | 1120 | 1.2352 | 0.5484 |
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+ | 0.9394 | 10.01 | 1232 | 1.2345 | 0.4977 |
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+ | 0.8285 | 11.01 | 1344 | 0.8702 | 0.6682 |
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+ | 1.1175 | 12.01 | 1456 | 0.9073 | 0.6406 |
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+ | 1.093 | 13.01 | 1568 | 0.9210 | 0.5576 |
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+ | 0.8364 | 14.01 | 1680 | 0.9316 | 0.6590 |
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+ | 0.766 | 15.01 | 1792 | 0.7628 | 0.7742 |
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+ | 0.7702 | 16.01 | 1904 | 0.8982 | 0.6682 |
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+ | 0.9184 | 17.01 | 2016 | 1.1010 | 0.6221 |
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+ | 0.7309 | 18.01 | 2128 | 0.8245 | 0.6866 |
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+ | 0.9575 | 19.01 | 2240 | 0.9029 | 0.7097 |
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+ | 0.8233 | 20.01 | 2352 | 1.2445 | 0.5161 |
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+ | 0.7643 | 21.01 | 2464 | 0.9558 | 0.6498 |
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+ | 0.6722 | 22.01 | 2576 | 1.1864 | 0.5714 |
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+ | 0.8441 | 23.01 | 2688 | 0.9690 | 0.7235 |
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+ | 0.7971 | 24.01 | 2800 | 0.9349 | 0.6774 |
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+ | 0.8296 | 25.01 | 2912 | 1.4574 | 0.4516 |
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+ | 0.8613 | 26.01 | 3024 | 0.8688 | 0.7189 |
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+ | 0.5614 | 27.01 | 3136 | 1.2101 | 0.6083 |
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+ | 0.6971 | 28.01 | 3248 | 1.3006 | 0.4654 |
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+ | 0.9642 | 29.01 | 3360 | 0.9573 | 0.6313 |
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+ | 0.836 | 30.01 | 3472 | 1.1268 | 0.6221 |
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+ | 0.7166 | 31.01 | 3584 | 1.2384 | 0.5622 |
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+ | 0.9302 | 32.01 | 3696 | 1.0620 | 0.5991 |
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+ | 0.7729 | 33.01 | 3808 | 1.3253 | 0.5622 |
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+ | 0.8005 | 34.01 | 3920 | 1.4979 | 0.4931 |
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+ | 0.8025 | 35.01 | 4032 | 0.9786 | 0.5668 |
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+ | 0.881 | 36.01 | 4144 | 0.8477 | 0.6544 |
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+ | 0.5343 | 37.01 | 4256 | 1.3107 | 0.6544 |
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+ | 0.5611 | 38.01 | 4368 | 0.9520 | 0.6866 |
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+ | 0.6824 | 39.01 | 4480 | 0.7909 | 0.7281 |
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+ | 0.6146 | 40.01 | 4592 | 1.0886 | 0.6175 |
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+ | 1.0098 | 41.01 | 4704 | 1.0434 | 0.6313 |
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+ | 0.5555 | 42.01 | 4816 | 0.9603 | 0.6912 |
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+ | 0.4578 | 43.01 | 4928 | 1.2341 | 0.5945 |
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+ | 0.5883 | 44.01 | 5040 | 1.2559 | 0.6359 |
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+ | 0.3579 | 45.01 | 5152 | 1.2459 | 0.5622 |
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+ | 0.7936 | 46.01 | 5264 | 1.2685 | 0.6083 |
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+ | 0.4331 | 47.01 | 5376 | 0.9118 | 0.7097 |
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+ | 0.8989 | 48.01 | 5488 | 1.3406 | 0.5806 |
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+ | 0.7674 | 49.01 | 5600 | 1.5231 | 0.5484 |
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+ | 0.8136 | 50.01 | 5712 | 1.2210 | 0.6221 |
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+ | 0.6583 | 51.01 | 5824 | 0.9262 | 0.7051 |
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+ | 0.4305 | 52.01 | 5936 | 1.0339 | 0.6959 |
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+ | 0.7197 | 53.01 | 6048 | 1.1948 | 0.6682 |
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+ | 0.7143 | 54.01 | 6160 | 1.1851 | 0.6774 |
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+ | 0.5441 | 55.01 | 6272 | 1.0351 | 0.6636 |
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+ | 0.6443 | 56.01 | 6384 | 1.0297 | 0.6866 |
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+ | 0.7747 | 57.01 | 6496 | 1.5174 | 0.5991 |
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+ | 0.5943 | 58.01 | 6608 | 1.1961 | 0.6452 |
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+ | 0.5781 | 59.01 | 6720 | 1.2187 | 0.7143 |
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+ | 0.6913 | 60.01 | 6832 | 1.1590 | 0.6728 |
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+ | 0.6186 | 61.01 | 6944 | 1.0495 | 0.7235 |
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+ | 0.5185 | 62.01 | 7056 | 0.9844 | 0.7051 |
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+ | 0.4077 | 63.01 | 7168 | 1.3194 | 0.6313 |
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+ | 0.8217 | 64.01 | 7280 | 1.2620 | 0.6636 |
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+ | 0.5273 | 65.01 | 7392 | 1.0395 | 0.7373 |
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+ | 0.9002 | 66.01 | 7504 | 1.5225 | 0.5806 |
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+ | 0.5763 | 67.01 | 7616 | 1.2559 | 0.6406 |
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+ | 1.0535 | 68.01 | 7728 | 1.2646 | 0.6498 |
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+ | 1.0064 | 69.01 | 7840 | 1.1533 | 0.6866 |
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+ | 0.332 | 70.01 | 7952 | 1.0438 | 0.7005 |
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+ | 0.3978 | 71.01 | 8064 | 1.0248 | 0.7051 |
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+ | 0.4459 | 72.01 | 8176 | 1.0926 | 0.7465 |
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+ | 0.511 | 73.01 | 8288 | 1.1233 | 0.7143 |
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+ | 0.7933 | 74.01 | 8400 | 1.1535 | 0.7189 |
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+ | 0.3739 | 75.01 | 8512 | 1.3056 | 0.6912 |
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+ | 0.6976 | 76.01 | 8624 | 1.3159 | 0.6682 |
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+ | 0.5453 | 77.01 | 8736 | 1.4541 | 0.6359 |
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+ | 0.2915 | 78.01 | 8848 | 1.2601 | 0.7051 |
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+ | 0.6552 | 79.01 | 8960 | 1.5338 | 0.6544 |
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+ | 0.5067 | 80.01 | 9072 | 1.6630 | 0.6037 |
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+ | 0.5134 | 81.01 | 9184 | 1.4740 | 0.6406 |
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+ | 0.7271 | 82.01 | 9296 | 1.2171 | 0.7097 |
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+ | 0.719 | 83.01 | 9408 | 1.3653 | 0.6406 |
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+ | 0.1955 | 84.01 | 9520 | 1.4696 | 0.6544 |
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+ | 0.5761 | 85.01 | 9632 | 1.3334 | 0.6636 |
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+ | 0.7094 | 86.01 | 9744 | 1.2673 | 0.6912 |
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+ | 0.5186 | 87.01 | 9856 | 1.3147 | 0.6866 |
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+ | 0.6876 | 88.01 | 9968 | 1.2622 | 0.7051 |
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+ | 0.4912 | 89.01 | 10080 | 1.3054 | 0.7189 |
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+ | 0.194 | 90.01 | 10192 | 1.3244 | 0.6959 |
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+ | 0.6916 | 91.01 | 10304 | 1.1800 | 0.7327 |
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+ | 0.5735 | 92.01 | 10416 | 1.1056 | 0.7419 |
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+ | 0.2122 | 93.01 | 10528 | 1.1070 | 0.7281 |
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+ | 0.1434 | 94.01 | 10640 | 1.1776 | 0.7097 |
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+ | 0.4681 | 95.01 | 10752 | 1.1505 | 0.7327 |
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+ | 0.2856 | 96.01 | 10864 | 1.1203 | 0.7235 |
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+ | 0.6509 | 97.01 | 10976 | 1.1502 | 0.7189 |
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+ | 0.1881 | 98.01 | 11088 | 1.1474 | 0.7189 |
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+ | 0.5577 | 99.0 | 11100 | 1.1473 | 0.7189 |
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  ### Framework versions
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