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videomae-base-finetuned-ASBD_ESBD

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: 1.2426
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- - Accuracy: 0.4444
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  ## Model description
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@@ -43,22 +43,58 @@ 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: 100
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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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- | 1.4676 | 0.11 | 11 | 1.3483 | 0.1940 |
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- | 1.3041 | 1.11 | 22 | 1.3224 | 0.2985 |
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- | 1.3033 | 2.11 | 33 | 1.3198 | 0.3134 |
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- | 1.2272 | 3.11 | 44 | 1.2533 | 0.2985 |
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- | 1.0746 | 4.11 | 55 | 1.1831 | 0.4328 |
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- | 0.9408 | 5.11 | 66 | 1.1709 | 0.5672 |
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- | 0.8244 | 6.11 | 77 | 1.0891 | 0.5970 |
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- | 0.6983 | 7.11 | 88 | 1.1408 | 0.5522 |
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- | 0.6206 | 8.11 | 99 | 1.0971 | 0.5373 |
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- | 0.5959 | 9.01 | 100 | 1.0966 | 0.5373 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 1.4178
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+ - Accuracy: 0.5714
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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: 500
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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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+ | 1.504 | 0.02 | 11 | 1.3929 | 0.3433 |
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+ | 1.3539 | 1.02 | 22 | 1.3380 | 0.1940 |
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+ | 1.2957 | 2.02 | 33 | 1.3519 | 0.2239 |
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+ | 1.2368 | 3.02 | 44 | 1.3220 | 0.3881 |
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+ | 1.1561 | 4.02 | 55 | 1.2803 | 0.3134 |
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+ | 1.0195 | 5.02 | 66 | 1.2588 | 0.5373 |
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+ | 0.8594 | 6.02 | 77 | 1.1591 | 0.5075 |
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+ | 0.8756 | 7.02 | 88 | 0.9532 | 0.6119 |
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+ | 0.6488 | 8.02 | 99 | 1.1922 | 0.5373 |
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+ | 0.4427 | 9.02 | 110 | 0.9780 | 0.6567 |
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+ | 0.3975 | 10.02 | 121 | 1.3228 | 0.5373 |
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+ | 0.3978 | 11.02 | 132 | 1.2083 | 0.6418 |
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+ | 0.2859 | 12.02 | 143 | 1.0027 | 0.7463 |
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+ | 0.3441 | 13.02 | 154 | 1.3718 | 0.5821 |
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+ | 0.2239 | 14.02 | 165 | 1.4324 | 0.5821 |
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+ | 0.2275 | 15.02 | 176 | 1.1823 | 0.6418 |
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+ | 0.1734 | 16.02 | 187 | 1.5484 | 0.6119 |
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+ | 0.2451 | 17.02 | 198 | 1.3764 | 0.5821 |
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+ | 0.1317 | 18.02 | 209 | 1.3731 | 0.6716 |
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+ | 0.0778 | 19.02 | 220 | 1.3567 | 0.7164 |
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+ | 0.1963 | 20.02 | 231 | 1.0905 | 0.7164 |
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+ | 0.1474 | 21.02 | 242 | 2.1361 | 0.4627 |
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+ | 0.0487 | 22.02 | 253 | 1.2189 | 0.7164 |
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+ | 0.0699 | 23.02 | 264 | 1.7618 | 0.5970 |
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+ | 0.1576 | 24.02 | 275 | 1.1939 | 0.7463 |
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+ | 0.0377 | 25.02 | 286 | 1.2287 | 0.7313 |
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+ | 0.0674 | 26.02 | 297 | 1.5247 | 0.6567 |
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+ | 0.0188 | 27.02 | 308 | 1.7585 | 0.6567 |
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+ | 0.0681 | 28.02 | 319 | 1.7868 | 0.6567 |
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+ | 0.0341 | 29.02 | 330 | 1.3745 | 0.6567 |
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+ | 0.05 | 30.02 | 341 | 1.8781 | 0.6418 |
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+ | 0.0269 | 31.02 | 352 | 1.9228 | 0.5970 |
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+ | 0.0213 | 32.02 | 363 | 1.8014 | 0.6119 |
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+ | 0.0061 | 33.02 | 374 | 1.4477 | 0.6866 |
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+ | 0.0338 | 34.02 | 385 | 1.5303 | 0.6567 |
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+ | 0.0086 | 35.02 | 396 | 1.5219 | 0.7015 |
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+ | 0.0891 | 36.02 | 407 | 1.8414 | 0.5821 |
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+ | 0.0032 | 37.02 | 418 | 1.8731 | 0.5821 |
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+ | 0.0028 | 38.02 | 429 | 1.6881 | 0.6418 |
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+ | 0.0434 | 39.02 | 440 | 1.7288 | 0.6567 |
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+ | 0.0018 | 40.02 | 451 | 1.8235 | 0.6119 |
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+ | 0.0232 | 41.02 | 462 | 1.8903 | 0.6119 |
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+ | 0.0016 | 42.02 | 473 | 1.9292 | 0.6119 |
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+ | 0.0016 | 43.02 | 484 | 1.9059 | 0.6119 |
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+ | 0.0029 | 44.02 | 495 | 1.8093 | 0.6418 |
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+ | 0.0519 | 45.01 | 500 | 1.8045 | 0.6418 |
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  ### Framework versions