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videomae-base-finetuned-ucf101-subset

Video Classification
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PyTorch
TensorBoard
videomae
Generated from Trainer
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update model card README.md

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@@ -16,8 +16,8 @@ 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: 0.4287
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- - Accuracy: 0.8387
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  ## Model description
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@@ -43,16 +43,62 @@ 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: 300
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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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- | 1.6613 | 0.25 | 75 | 1.3506 | 0.5286 |
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- | 0.4175 | 1.25 | 150 | 0.5572 | 0.7286 |
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- | 0.3592 | 2.25 | 225 | 0.2464 | 0.9286 |
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- | 0.1935 | 3.25 | 300 | 0.2603 | 0.9 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.1051
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+ - Accuracy: 0.9677
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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: 3750
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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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+ | 2.3017 | 0.02 | 75 | 2.3034 | 0.1286 |
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+ | 1.9471 | 1.02 | 150 | 1.9157 | 0.3714 |
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+ | 0.9752 | 2.02 | 225 | 0.9294 | 0.6714 |
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+ | 0.4076 | 3.02 | 300 | 0.3997 | 0.8143 |
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+ | 0.4284 | 4.02 | 375 | 0.6502 | 0.7714 |
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+ | 0.1713 | 5.02 | 450 | 0.3226 | 0.8714 |
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+ | 0.4188 | 6.02 | 525 | 0.6348 | 0.8429 |
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+ | 0.0101 | 7.02 | 600 | 0.4137 | 0.9143 |
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+ | 0.0126 | 8.02 | 675 | 0.1349 | 0.9429 |
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+ | 0.0444 | 9.02 | 750 | 1.0845 | 0.8 |
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+ | 0.0023 | 10.02 | 825 | 0.1856 | 0.9 |
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+ | 0.0018 | 11.02 | 900 | 0.2485 | 0.9143 |
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+ | 0.0017 | 12.02 | 975 | 0.0677 | 0.9714 |
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+ | 0.0429 | 13.02 | 1050 | 0.1233 | 0.9286 |
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+ | 0.0035 | 14.02 | 1125 | 0.3002 | 0.9571 |
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+ | 0.0013 | 15.02 | 1200 | 0.1211 | 0.9857 |
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+ | 0.001 | 16.02 | 1275 | 0.1264 | 0.9714 |
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+ | 0.0013 | 17.02 | 1350 | 0.2979 | 0.9429 |
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+ | 0.0714 | 18.02 | 1425 | 0.4232 | 0.9143 |
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+ | 0.0011 | 19.02 | 1500 | 0.3415 | 0.9286 |
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+ | 0.001 | 20.02 | 1575 | 0.1736 | 0.9286 |
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+ | 0.0007 | 21.02 | 1650 | 0.2461 | 0.9429 |
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+ | 0.0008 | 22.02 | 1725 | 0.3369 | 0.9286 |
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+ | 0.0007 | 23.02 | 1800 | 0.1453 | 0.9571 |
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+ | 0.0007 | 24.02 | 1875 | 0.1013 | 0.9857 |
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+ | 0.0006 | 25.02 | 1950 | 0.1100 | 0.9857 |
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+ | 0.0006 | 26.02 | 2025 | 0.1088 | 0.9857 |
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+ | 0.0006 | 27.02 | 2100 | 0.1165 | 0.9714 |
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+ | 0.0006 | 28.02 | 2175 | 0.0660 | 0.9857 |
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+ | 0.0007 | 29.02 | 2250 | 0.2951 | 0.9429 |
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+ | 0.0005 | 30.02 | 2325 | 0.0896 | 0.9857 |
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+ | 0.0007 | 31.02 | 2400 | 0.1059 | 0.9857 |
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+ | 0.0005 | 32.02 | 2475 | 0.0989 | 0.9857 |
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+ | 0.0005 | 33.02 | 2550 | 0.0771 | 0.9857 |
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+ | 0.0005 | 34.02 | 2625 | 0.0759 | 0.9857 |
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+ | 0.0005 | 35.02 | 2700 | 0.0803 | 0.9857 |
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+ | 0.0004 | 36.02 | 2775 | 0.0892 | 0.9857 |
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+ | 0.0004 | 37.02 | 2850 | 0.1913 | 0.9571 |
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+ | 0.0004 | 38.02 | 2925 | 0.4228 | 0.9429 |
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+ | 0.0004 | 39.02 | 3000 | 0.4060 | 0.9429 |
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+ | 0.0004 | 40.02 | 3075 | 0.3824 | 0.9429 |
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+ | 0.0951 | 41.02 | 3150 | 0.4202 | 0.9429 |
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+ | 0.0004 | 42.02 | 3225 | 0.1987 | 0.9571 |
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+ | 0.0004 | 43.02 | 3300 | 0.1764 | 0.9571 |
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+ | 0.0004 | 44.02 | 3375 | 0.1509 | 0.9571 |
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+ | 0.0004 | 45.02 | 3450 | 0.1498 | 0.9571 |
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+ | 0.0004 | 46.02 | 3525 | 0.1441 | 0.9714 |
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+ | 0.0004 | 47.02 | 3600 | 0.1332 | 0.9714 |
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+ | 0.0004 | 48.02 | 3675 | 0.1573 | 0.9714 |
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+ | 0.0003 | 49.02 | 3750 | 0.1616 | 0.9714 |
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  ### Framework versions